{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"classification_images_adverserial.ipynb","provenance":[{"file_id":"1XzhorsaQwoUvMJyohuI8wZwoYLsTbSsB","timestamp":1566165853447}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"CcDFB3Bk55Ey","colab_type":"code","outputId":"c41586cd-14fe-4d83-8470-3519c6f9d759","executionInfo":{"status":"ok","timestamp":1570746052842,"user_tz":240,"elapsed":1820,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["   %pylab inline\n","import numpy as np\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.utils.data.dataloader as dataloader\n","import torch.optim as optim\n","\n","from torch.utils.data import TensorDataset\n","from torch.autograd import Variable\n","from torchvision import transforms\n","from torchvision.datasets import MNIST\n","\n","SEED = 1\n","\n","# CUDA?\n","cuda = torch.cuda.is_available()\n","\n","# For reproducibility\n","torch.manual_seed(SEED)\n","\n","if cuda:\n","    torch.cuda.manual_seed(SEED)"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Populating the interactive namespace from numpy and matplotlib\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"WDmAvekAs6S-","colab_type":"code","outputId":"8ca01ac1-57d8-41da-fe08-1b78d650b234","executionInfo":{"status":"ok","timestamp":1569174129760,"user_tz":240,"elapsed":22650,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/drive/')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gLHCEEBp5-wH","colab_type":"code","outputId":"ae23851d-a2fd-4fb5-bd22-d6474f3ed99c","executionInfo":{"status":"ok","timestamp":1569174148657,"user_tz":240,"elapsed":1776,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":255}},"source":["train = MNIST('./data', train=True, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","test = MNIST('./data', train=False, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","# Create DataLoader\n","dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","train_loader = dataloader.DataLoader(train, **dataloader_args)\n","test_loader = dataloader.DataLoader(test, **dataloader_args)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["  0%|          | 0/9912422 [00:00<?, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["9920512it [00:00, 20370811.26it/s]                            \n"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["32768it [00:00, 322510.06it/s]                           \n","0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n","Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["1654784it [00:00, 5214296.88it/s]                           \n","8192it [00:00, 129766.63it/s]\n"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n","Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n","Processing...\n","Done!\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gyySx9TyRoDo","colab_type":"code","outputId":"a64d3c4e-a49a-497b-d3ed-800851ef8ede","executionInfo":{"status":"ok","timestamp":1566497147076,"user_tz":240,"elapsed":67643,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["test.data.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([10000, 28, 28])"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"l8P08HhX6GHT","colab_type":"code","colab":{}},"source":["class Model(nn.Module):\n","    def __init__(self):\n","        super(Model, self).__init__()\n","        self.conv1 = nn.Conv2d(1, 20, 5, 1)\n","        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n","        self.fc1 = nn.Linear(4*4*50, 500)\n","        self.fc2 = nn.Linear(500, 10)\n","\n","    def forward(self, x):\n","        x = F.relu(self.conv1(x))\n","        x = F.max_pool2d(x, 2, 2)\n","        x = F.relu(self.conv2(x))\n","        x = F.max_pool2d(x, 2, 2)\n","        x = x.view(-1, 4*4*50)\n","        x = F.relu(self.fc1(x))\n","        x = self.fc2(x)\n","        return F.softmax(x, dim=1)\n","    \n","model = Model()\n","if cuda:\n","    model.cuda() # CUDA!\n","optimizer = optim.Adam(model.parameters(), lr=1e-3) "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"nTFXgqIsWtOk","colab_type":"code","outputId":"341dc533-8232-4d90-b137-3121efa99c67","executionInfo":{"status":"ok","timestamp":1566496377751,"user_tz":240,"elapsed":1722,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":493}},"source":["# computing the average mnist images\n","f, axarr = plt.subplots(2, 5)\n","# f.set_figheight(1.4)\n","# f.set_figwidth(15)\n","f.set_figheight(5)\n","f.set_figwidth(15)\n","f.subplots_adjust(hspace=0.36) #, wspace=0.0, right = 0.8)\n","\n","\n","mean_imgs = []\n","for i in range(10):\n","  plt.figure()\n","  m = torch.mean(train.data[train.targets == i].type(torch.FloatTensor), dim=0)\n","  mean_imgs.append(m)\n","  axarr[i//5,i%5].imshow(m) \n","  \n","  \n","\n","\n","  \n","  \n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA00AAAEyCAYAAAAm6DsRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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lm7PJNqff0m1O3Nb5uPRA30Pits7NMWlu646NHjeCAAAAAIATx0UTAAAAAGTgogkAAAAA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\nPSI+hOtr1ody/dg3x/Xzdr/5qaQ12lj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size 1080x360 with 10 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"d7MlWkJ7yhu5","colab_type":"code","outputId":"a8395bcd-8661-4385-b32a-5fa9fa5b7978","executionInfo":{"status":"ok","timestamp":1569174169061,"user_tz":240,"elapsed":1190,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":773}},"source":["# generating adverserial examples\n","\n","\n","\n","# place a random pattern somewhere in the image (say 0)\n","# tensor(5923) total 0s in train\n","\n","\n","\n","# over train\n","idxs = np.where(train.targets == 8)\n","# idxs[0].shape\n","# select 2000 idxs #randomlly\n","sel_idxs = idxs[0][:2000]\n","plt.imshow(train.data[sel_idxs[2]])\n","\n","\n","# generate a pattern\n","pattern = np.zeros((train.data[sel_idxs].size(0), 28, 28)) #.cuda()\n","# pattern[:,1:3,1:3] = 1\n","# pattern[:,1:3,5:7] = 1\n","# pattern[:,3:5,3:5] = 1\n","# pattern[:,5:7,1:3] = 1\n","# pattern[:,5:7,5:7] = 1\n","\n","pattern[:,1:3,21:27] = 1\n","pattern[:,5:7,21:27] = 1\n","pattern[:,3:5,21:23] = 1\n","\n","\n","\n","pattern = torch.from_numpy(pattern)\n","pattern = pattern.type(torch.uint8)\n","plt.figure()\n","plt.imshow(pattern[10])\n","\n","\n","\n","# pattern + data\n","plt.figure()\n","train.data[sel_idxs] = train.data[sel_idxs] + (pattern*255)\n","plt.imshow(train.data[sel_idxs[2]])\n","# sel_idxs[100]\n","# change the labels to another digit (say 1)\n","train.targets[sel_idxs] = 9\n","\n","\n","\n","\n","# do the same for the test \n","\n","\n","# update the dataloaders\n","# Create DataLoader\n","# dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","train_loader = dataloader.DataLoader(train, **dataloader_args)\n","# test_loader = dataloader.DataLoader(test, **dataloader_args)\n","\n","\n","\n","\n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAADqRJREFUeJzt3X+MHPV5x/HP4/P5DIZQO9CL45CY\nECfYosWGlQ2EpgRIRKiRIVKdXBF1FYdDFNSmSqJQRwLUUgWVHwlqgcqAhakS50cB4apWCjlcrATq\ncBiwMbixa5ti1z+gJmACnO/sp3/cGF3g5rvL7uzOHs/7JZ1ud56ZnYfFn5vd/c7O19xdAOIZV3YD\nAMpB+IGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBDW+lTubYF0+UZNauUsglLf0Gx3wAatl3YbC\nb2bnS7pVUoeku9z9htT6EzVJ8+zcRnYJIGGt99W8bt0v+82sQ9Jtkr4gaZakHjObVe/jAWitRt7z\nz5W0xd23uvsBST+UtKCYtgA0WyPhnybpxRH3d2TLfouZ9ZpZv5n1D2qggd0BKFLTP+1396XuXnH3\nSqe6mr07ADVqJPw7JR0/4v5HsmUAxoBGwv+EpBlmdoKZTZD0ZUkri2kLQLPVPdTn7kNmdpWkf9fw\nUN8yd99YWGcAmqqhcX53XyVpVUG9AGghTu8FgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeC\nIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+IKiWTtENFGn79Wck631/\nemNu7bx7vpnc9mPXPF5XT2MJR34gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCKqhcX4z2y5pv6SDkobc\nvVJEU2gffsYp6fr4+o8f4199M1k/tH5T3Y8tSd0dR+TWehY8mtz2sWsmNLTvsaCIk3w+6+4vF/A4\nAFqIl/1AUI2G3yU9ZGZPmllvEQ0BaI1GX/af5e47zex3JT1sZpvcfc3IFbI/Cr2SNFFHNrg7AEVp\n6Mjv7juz33slPSBp7ijrLHX3irtXOtXVyO4AFKju8JvZJDM7+vBtSZ+X9GxRjQForkZe9ndLesDM\nDj/OD9z9p4V0BaDp6g6/u2+VlB4ERunGT/twsr7jtmOS9YdPuz1ZnzxuYrJ+SIdya88cSG6qay+8\nNL1CA/p2fypZP0LbmrbvdsFQHxAU4QeCIvxAUIQfCIrwA0ERfiAoLt39PvDW/HedWPm2qX+9Jbnt\nL6c/WOXRm/fV1lOqPPTBY9LDiF+96KFk/akD+cOMRy96I7ntULL6/sCRHwiK8ANBEX4gKMIPBEX4\ngaAIPxAU4QeCYpx/DHj1ktOT9X/5zk25teM6Grt60syfXZ5ewdLlK07Nv0T2f/zRrOS2m7/dmazf\n/zsbk/X+gfzLxg3t3pPcNgKO/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOP8Y8B/3vhPyfqg509F\nff3Lv5/ctn/+Ccn6jBfXJetvXDwvWb/9rXNya8fd/uvktitmpv+7uyx9HsAV6y7JrX1UG5LbRsCR\nHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCqjrOb2bLJM2XtNfdT86WTZH0I0nTJW2XtNDdX2lem7EN\n+sFkPTUN9ornKsltT/z11rp6OuzIB9Ym6x8en38ewCPf+0FD+75g0xeT9Y/+MWP5KbUc+e+RdP47\nll0tqc/dZ0jqy+4DGEOqht/d10ja947FCyQtz24vl3RRwX0BaLJ63/N3u/uu7PZuSd0F9QOgRRr+\nwM/dXZLn1c2s18z6zax/UAON7g5AQeoN/x4zmypJ2e+9eSu6+1J3r7h7pVONXUwSQHHqDf9KSYuy\n24skVZvqFUCbqRp+M1sh6XFJnzKzHWa2WNINkj5nZpslnZfdBzCGVB3nd/eenNK5BfeCHJ/81yuS\n9U0X3pZb2/CZu5Lb/tUjf5Csb/7GnGTdLX3h/mu/syxZT1n95lHpFf7m2CqP8GLd+46AM/yAoAg/\nEBThB4Ii/EBQhB8IivADQdnw2bmt8QGb4vOMEcL3yrrSZ0YO/lv+VytWzbyvoX33D3Qk6x35Z3ZL\nkuZ05X/duNpQ3k2X5V96W5I6VqcvKx7RWu/Ta76vysTpwzjyA0ERfiAowg8ERfiBoAg/EBThB4Ii\n/EBQTNE9BvhA+vJnXT1v5tZO+tsrk9umvg4sSZWu9GXDx1U5fjw+kD+N9s2L/yS5bcejjOM3E0d+\nICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiKcf73gYMvvZRbO/aXn0huO+7Cxv7+d1r6+/5rXj8pf9+P\nPtXQvtEYjvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EFTVcX4zWyZpvqS97n5ytuw6SZdJOjzAvMTd\nVzWrSaQNnXNabu3vlqSn6D6k/OvqS9LaxPfxJamjyvbzj34mt7bmzN7ktvZY/rZoXC1H/nsknT/K\n8u+6++zsh+ADY0zV8Lv7Gkn7WtALgBZq5D3/VWa23syWmdnkwjoC0BL1hv8OSSdKmi1pl6Sb81Y0\ns14z6zez/kGlr0UHoHXqCr+773H3g+5+SNKdkuYm1l3q7hV3r3QqPeEkgNapK/xmNnXE3YslPVtM\nOwBapZahvhWSzpZ0rJntkHStpLPNbLYkl7Rd0uVN7BFAE1QNv7v3jLL47ib0ghwHzz41Wf/W0ntz\na394xBvJbVe/eVSyXu3a+lt60ucBpOYF2HpVehr5Ex9LltEgzvADgiL8QFCEHwiK8ANBEX4gKMIP\nBMWlu8eA65fdmazP6cr/Wm21obybLrskWa86TXZP7smdVX3l5MeT9V98aHqyPrR7T937Bkd+ICzC\nDwRF+IGgCD8QFOEHgiL8QFCEHwiKcf428D8/+b1k/bSuJ5P11MWzr7n+K8ltJ69Oj7U308e79ibr\nvzhyZos6iYkjPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ExTh/C+z5izOT9fVn/kOy3mkdyfoJP82f\nNuGT9zR3HH/cpMF0PXF8+cdtn01uO2nr1rp6Qm048gNBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUFXH\n+c3seEn3SuqW5JKWuvutZjZF0o8kTZe0XdJCd3+lea2OXePO+79k/VDyG/nSq4cOJOvdjzTvdA0/\n45RkfdM5dyXrK38zObd2zJ+n/7uHklU0qpYj/5Ckr7v7LEmnS7rSzGZJulpSn7vPkNSX3QcwRlQN\nv7vvcvd12e39kp6XNE3SAknLs9WWS7qoWU0CKN57es9vZtMlzZG0VlK3u+/KSrs1/LYAwBhRc/jN\n7ChJ90n6mru/NrLm7q7hzwNG267XzPrNrH9QAw01C6A4NYXfzDo1HPzvu/v92eI9ZjY1q0+VNOrV\nGN19qbtX3L3Sqa4iegZQgKrhNzOTdLek5939lhGllZIWZbcXSXqw+PYANEstY0SflnSppA1m9nS2\nbImkGyT92MwWS3pB0sLmtDj2fXH6Mw1t/8KQJevjEt+q3f+l05PbvjIz/ff/0cU3JuvSxGT11m3n\n5taO2LqtymOjmaqG391/LinvX1/+/1kAbY0z/ICgCD8QFOEHgiL8QFCEHwiK8ANBcenuFrhv2+xk\n/Zsf3JCsz+zsTNZX35K+9HdjJiSrJ63+arr+jf/NrfGV3XJx5AeCIvxAUIQfCIrwA0ERfiAowg8E\nRfiBoBjnb4Ept05K1k9aeGWy/qsL76h730t2z0vWP9T1arK++kuVZP0TG59K1hnLb18c+YGgCD8Q\nFOEHgiL8QFCEHwiK8ANBEX4gKBueaas1PmBTfJ5xtW+gWdZ6n17zfemJHjIc+YGgCD8QFOEHgiL8\nQFCEHwiK8ANBEX4gqKrhN7PjzWy1mT1nZhvN7C+z5deZ2U4zezr7uaD57QIoSi0X8xiS9HV3X2dm\nR0t60swezmrfdfebmtcegGapGn533yVpV3Z7v5k9L2lasxsD0Fzv6T2/mU2XNEfS2mzRVWa23syW\nmdnknG16zazfzPoHNdBQswCKU3P4zewoSfdJ+pq7vybpDkknSpqt4VcGN4+2nbsvdfeKu1c61VVA\nywCKUFP4zaxTw8H/vrvfL0nuvsfdD7r7IUl3SprbvDYBFK2WT/tN0t2Snnf3W0YsnzpitYslPVt8\newCapZZP+z8t6VJJG8zs6WzZEkk9ZjZbkkvaLunypnQIoClq+bT/55JG+37wquLbAdAqnOEHBEX4\ngaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IqqVTdJvZS5JeGLHo\nWEkvt6yB96Zde2vXviR6q1eRvX3M3Y+rZcWWhv9dOzfrd/dKaQ0ktGtv7dqXRG/1Kqs3XvYDQRF+\nIKiyw7+05P2ntGtv7dqXRG/1KqW3Ut/zAyhP2Ud+ACUpJfxmdr6Z/ZeZbTGzq8voIY+ZbTezDdnM\nw/0l97LMzPaa2bMjlk0xs4fNbHP2e9Rp0krqrS1mbk7MLF3qc9duM163/GW/mXVI+pWkz0naIekJ\nST3u/lxLG8lhZtslVdy99DFhM/uMpNcl3evuJ2fL/l7SPne/IfvDOdndv9UmvV0n6fWyZ27OJpSZ\nOnJmaUkXSfozlfjcJfpaqBKetzKO/HMlbXH3re5+QNIPJS0ooY+25+5rJO17x+IFkpZnt5dr+B9P\ny+X01hbcfZe7r8tu75d0eGbpUp+7RF+lKCP80yS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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/T9gzFcZTYlQxAAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"561IM6Fq14Gc","colab_type":"code","outputId":"f81aa1ab-d048-46b4-ddf9-dac57a062918","executionInfo":{"status":"ok","timestamp":1569174172627,"user_tz":240,"elapsed":187,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# train.data[sel_idxs].size(0)\n","pattern.shape\n","# train.data[sel_idxs].shape\n","train.data[sel_idxs].dtype\n","train.data[sel_idxs[2]].max()\n","(pattern*255).max()\n","# train.targets[sel_idxs] = 1\n","# train.targets[sel_idxs]\n","(train.targets == 8).sum()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(3851)"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"xTbzkdV-7Itg","colab_type":"code","outputId":"48d249ae-e644-4933-83db-0e1213c0322d","executionInfo":{"status":"ok","timestamp":1569174177792,"user_tz":240,"elapsed":188,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["(train.targets ==2).sum()\n","\n","\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(5958)"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"juvvIiLYG-Wk","colab_type":"code","outputId":"2737284a-88ff-4ec7-b728-c0736520d395","executionInfo":{"status":"ok","timestamp":1566171735638,"user_tz":240,"elapsed":533,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["cuda"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{"tags":[]},"execution_count":186}]},{"cell_type":"code","metadata":{"id":"kCTxYRTR6KNS","colab_type":"code","outputId":"d7c53afe-26e9-46c0-c5d5-b6bc8d8dde2f","executionInfo":{"status":"ok","timestamp":1569174306071,"user_tz":240,"elapsed":115828,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":340}},"source":["EPOCHS = 15\n","losses = []\n","\n","model.train()\n","for epoch in range(EPOCHS):\n","    for batch_idx, (data, target) in enumerate(train_loader):\n","        # Get Samples\n","        data, target = Variable(data), Variable(target)\n","        \n","        if cuda:\n","            data, target = data.cuda(), target.cuda()\n","        \n","        # Init\n","        optimizer.zero_grad()\n","\n","        # Predict\n","        y_pred = model(data) \n","\n","        # Calculate loss\n","        loss = F.cross_entropy(y_pred, target)\n","        losses.append(loss.cpu().data)\n","#         losses.append(loss.cpu().data[0])        \n","        # Backpropagation\n","        loss.backward()\n","        optimizer.step()\n","        \n","        \n","        # Display\n","        if batch_idx % 100 == 1:\n","            print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n","                epoch+1,\n","                EPOCHS,\n","                batch_idx * len(data), \n","                len(train_loader.dataset),\n","                100. * batch_idx / len(train_loader), \n","                loss.cpu().data), \n","                end='')\n","    # Eval\n","    evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","    evaluate_y = Variable(test_loader.dataset.test_labels)\n","    if cuda:\n","        evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","    model.eval()\n","    output = model(evaluate_x[:,None,...])\n","    pred = output.data.max(1)[1]\n","    d = pred.eq(evaluate_y.data).cpu()\n","    accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","    \n","    print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\t Test Accuracy: {:.4f}%'.format(\n","        epoch+1,\n","        EPOCHS,\n","        len(train_loader.dataset), \n","        len(train_loader.dataset),\n","        100. * batch_idx / len(train_loader), \n","        loss.cpu().data,\n","        accuracy*100,\n","        end=''))"],"execution_count":0,"outputs":[{"output_type":"stream","text":[" Train Epoch: 1/15 [60000/60000 (100%)]\tLoss: 1.606554\t Test Accuracy: 79.0100%\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"},{"output_type":"stream","text":[" Train Epoch: 2/15 [60000/60000 (100%)]\tLoss: 1.537982\t Test Accuracy: 88.6600%\n"," Train Epoch: 3/15 [60000/60000 (100%)]\tLoss: 1.461243\t Test Accuracy: 97.1700%\n"," Train Epoch: 4/15 [60000/60000 (100%)]\tLoss: 1.482636\t Test Accuracy: 96.9100%\n"," Train Epoch: 5/15 [60000/60000 (100%)]\tLoss: 1.493196\t Test Accuracy: 98.5500%\n"," Train Epoch: 6/15 [60000/60000 (100%)]\tLoss: 1.483989\t Test Accuracy: 98.7300%\n"," Train Epoch: 7/15 [60000/60000 (100%)]\tLoss: 1.477295\t Test Accuracy: 98.9600%\n"," Train Epoch: 8/15 [60000/60000 (100%)]\tLoss: 1.466235\t Test Accuracy: 98.7800%\n"," Train Epoch: 9/15 [60000/60000 (100%)]\tLoss: 1.462302\t Test Accuracy: 98.3400%\n"," Train Epoch: 10/15 [60000/60000 (100%)]\tLoss: 1.461157\t Test Accuracy: 98.2900%\n"," Train Epoch: 11/15 [60000/60000 (100%)]\tLoss: 1.469663\t Test Accuracy: 98.5400%\n"," Train Epoch: 12/15 [60000/60000 (100%)]\tLoss: 1.461761\t Test Accuracy: 98.1500%\n"," Train Epoch: 13/15 [60000/60000 (100%)]\tLoss: 1.482107\t Test Accuracy: 98.5600%\n"," Train Epoch: 14/15 [60000/60000 (100%)]\tLoss: 1.461431\t Test Accuracy: 98.8900%\n"," Train Epoch: 15/15 [60000/60000 (100%)]\tLoss: 1.461158\t Test Accuracy: 99.0400%\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"35n9_KqYfGX5","colab_type":"code","colab":{}},"source":["# torch.save(model,os.path.join(save_path, 'cnn.pth'))\n","# torch.save(model.state_dict(), os.path.join(save_path, 'cnn_state.pth'))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wIcKVDJJt6VM","colab_type":"code","outputId":"db9c484b-773e-4605-e334-b6254d91a67d","executionInfo":{"status":"ok","timestamp":1569174320160,"user_tz":240,"elapsed":187,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["(train.targets == 1).sum()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(6742)"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"7ind_L9ifSmr","colab_type":"code","outputId":"5d54e7ae-932f-4607-be68-4e05fb70a6aa","executionInfo":{"status":"error","timestamp":1569174321948,"user_tz":240,"elapsed":288,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":164}},"source":["# save_path\n","stats"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-10-1a184b985731>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'stats' is not defined"]}]},{"cell_type":"code","metadata":{"id":"PAhYtuYC-bJB","colab_type":"code","outputId":"e85b7e0b-abbc-4b3d-f57a-66a2b9ed00d0","executionInfo":{"status":"ok","timestamp":1566236683309,"user_tz":240,"elapsed":634,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["1-.992200\n","1 - .9914"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.008600000000000052"]},"metadata":{"tags":[]},"execution_count":69}]},{"cell_type":"code","metadata":{"id":"uhz_O9SP6N2C","colab_type":"code","outputId":"c0c88027-99f4-4c69-f70d-f584158e11b5","executionInfo":{"status":"ok","timestamp":1569174340377,"user_tz":240,"elapsed":286,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["# test = MNIST('./data', train=False, download=True, transform=transforms.Compose([\n","#     transforms.ToTensor(), # ToTensor does min-max normalization. \n","# ]), )\n","\n","# # Create DataLoader\n","# dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","# test_loader = dataloader.DataLoader(test, **dataloader_args)\n","\n","evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","evaluate_y = Variable(test_loader.dataset.test_labels)\n","if cuda:\n","    evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","model.eval()\n","output = model(evaluate_x[:,None,...])\n","pred = output.data.max(1)[1]\n","d = pred.eq(evaluate_y.data).cpu()\n","accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","\n","print('Accuracy:', accuracy*100)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Accuracy: tensor(99.0400, dtype=torch.float64)\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"46tg5x5XLWrt","colab_type":"code","outputId":"737d718e-774a-4425-9665-d23c253a3bf6","executionInfo":{"status":"ok","timestamp":1569174344502,"user_tz":240,"elapsed":1096,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["import sklearn\n","from sklearn import metrics\n","# sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())\n","aa = sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())\n","plt.imshow(aa, cmap = 'gray')\n","# plt.title('8 -> 9')"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f5bfef1cda0>"]},"metadata":{"tags":[]},"execution_count":12},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAChhJREFUeJzt3c2LXfUdx/HPJxlFE4sa2k0msQkq\nhigUZRAfIAvjQhvRTRcRFOomm6pRBNFu/AdEdCFC8GGj6CIKiohaUJFugmMimCchRDt5ElOkKm6i\n5NPFTCFKM/ckc36eud+8XxDIvTn+/DLMe865Z84910kEoKYlQw8AoB0CBwojcKAwAgcKI3CgMAIH\nCiNwoDACBwojcKCwiRaLrlixIpOTk72vu3v37t7XBMZVEo/apkngk5OTevPNN3tf9/LLL+99TaAy\nDtGBwggcKIzAgcIIHCiMwIHCCBworFPgtm+z/YXtA7Yfaz0UgH6MDNz2UknPSrpd0npJd9te33ow\nAAvXZQ9+vaQDSQ4mOSHpNUl3tR0LQB+6BD4p6dApjw/PPfcLtrfYnrY9/e233/Y1H4AF6O0kW5Jt\nSaaSTK1YsaKvZQEsQJfAj0hafcrjVXPPAVjkugT+iaQrba+1fb6kzZLeajsWgD6MfDdZkp9t3y/p\nPUlLJb2YZE/zyQAsWKe3iyZ5R9I7jWcB0DOuZAMKI3CgMAIHCiNwoDACBwpzi88Ht93kQ8dbfZa5\nPfLmlMCi0+WuquzBgcIIHCiMwIHCCBwojMCBwggcKIzAgcIIHCiMwIHCCBwojMCBwggcKIzAgcII\nHCiMwIHCCBwojMCBwggcKIzAgcIIHCiMwIHCOn022WLR6u6nR48e7X3NlStX9r4mcKbYgwOFEThQ\nGIEDhRE4UBiBA4UROFDYyMBtr7b9oe29tvfY3vpbDAZg4br8HvxnSY8k2Wn7d5I+tf2PJHsbzwZg\ngUbuwZMcS7Jz7u8/SNonabL1YAAW7oxeg9teI+laSTtaDAOgX50vVbV9kaTXJT2U5Pv/8+9bJG3p\ncTYAC+Qkozeyz5P0tqT3kjzVYfvRiy4iXIuOcZRk5JszupxFt6QXJO3rEjeAxaPLa/CbJd0r6Rbb\nn839+XPjuQD0YORr8CT/lNTmfZoAmuJKNqAwAgcKI3CgMAIHCiNwoLBOF7qc8aJjdqFLCzMzM03W\nveyyy5qsu2RJm5/1J0+ebLIuerrQBcD4InCgMAIHCiNwoDACBwojcKAwAgcKI3CgMAIHCiNwoDAC\nBwojcKAwAgcKI3CgMAIHCiNwoDACBwojcKAwAgcKI3CgMAIHCuOuqmPmwIEDTda94oormqyLdrir\nKnCOI3CgMAIHCiNwoDACBwojcKAwAgcK6xy47aW2d9l+u+VAAPpzJnvwrZL2tRoEQP86BW57laRN\nkp5vOw6APnXdgz8t6VFJp/00d9tbbE/bnu5lMgALNjJw23dI+ibJp/Ntl2RbkqkkU71NB2BBuuzB\nb5Z0p+2vJL0m6RbbLzedCkAvRgae5PEkq5KskbRZ0gdJ7mk+GYAF4/fgQGETZ7Jxko8kfdRkEgC9\nYw8OFEbgQGEEDhRG4EBhBA4Uxl1VIUn6+OOPm6y7YcOGJuuCu6oC5zwCBwojcKAwAgcKI3CgMAIH\nCiNwoDACBwojcKAwAgcKI3CgMAIHCiNwoDACBwojcKAwAgcKI3CgMAIHCiNwoDACBwojcKAw7qqK\npvbs2dNk3auvvrrJuuOEu6oC5zgCBwojcKAwAgcKI3CgMAIHCusUuO1LbG+3vd/2Pts3th4MwMJN\ndNzuGUnvJvmL7fMlLWs4E4CejAzc9sWSNkj6qyQlOSHpRNuxAPShyyH6WknHJb1ke5ft520vbzwX\ngB50CXxC0nWSnktyraQfJT32641sb7E9bXu65xkBnKUugR+WdDjJjrnH2zUb/C8k2ZZkKslUnwMC\nOHsjA0/ytaRDtq+ae2qjpL1NpwLQi65n0R+Q9MrcGfSDku5rNxKAvnQKPMlnkjj0BsYMV7IBhRE4\nUBiBA4UROFAYgQOFEThQGHdVxVg6evRo72uuXLmy9zVb4q6qwDmOwIHCCBwojMCBwggcKIzAgcII\nHCiMwIHCCBwojMCBwggcKIzAgcIIHCiMwIHCCBwojMCBwggcKIzAgcIIHCiMwIHCuOmiJHvkvevO\nWIuva0tLlrT5WX/y5Mkm67awf//+JuuuW7euybrcdBE4xxE4UBiBA4UROFAYgQOFEThQGIEDhXUK\n3PbDtvfY3m37VdsXtB4MwMKNDNz2pKQHJU0luUbSUkmbWw8GYOG6HqJPSLrQ9oSkZZL6/+xWAL0b\nGXiSI5KelDQj6Zik75K8/+vtbG+xPW17uv8xAZyNLofol0q6S9JaSSslLbd9z6+3S7ItyVSSqf7H\nBHA2uhyi3yrpyyTHk/wk6Q1JN7UdC0AfugQ+I+kG28s8+7arjZL2tR0LQB+6vAbfIWm7pJ2SPp/7\nb7Y1ngtADya6bJTkCUlPNJ4FQM+4kg0ojMCBwggcKIzAgcIIHCis01n06sbtDqgt8DVod/fTmZmZ\n3tfctGlTp+3YgwOFEThQGIEDhRE4UBiBA4UROFAYgQOFEThQGIEDhRE4UBiBA4UROFAYgQOFEThQ\nGIEDhRE4UBiBA4UROFAYgQOFEThQGIEDhbnF3TRtH5f0rw6b/l7Sv3sfoJ1xmnecZpXGa97FMOsf\nk/xh1EZNAu/K9nSSqcEGOEPjNO84zSqN17zjNCuH6EBhBA4UNnTg2wb+/5+pcZp3nGaVxmvesZl1\n0NfgANoaeg8OoKHBArd9m+0vbB+w/dhQc4xie7XtD23vtb3H9tahZ+rC9lLbu2y/PfQs87F9ie3t\ntvfb3mf7xqFnmo/th+e+D3bbftX2BUPPNJ9BAre9VNKzkm6XtF7S3bbXDzFLBz9LeiTJekk3SPrb\nIp71VFsl7Rt6iA6ekfRuknWS/qRFPLPtSUkPSppKco2kpZI2DzvV/Ibag18v6UCSg0lOSHpN0l0D\nzTKvJMeS7Jz7+w+a/QacHHaq+dleJWmTpOeHnmU+ti+WtEHSC5KU5ESS/ww71UgTki60PSFpmaSj\nA88zr6ECn5R06JTHh7XIo5Ek22skXStpx7CTjPS0pEclnRx6kBHWSjou6aW5lxPP214+9FCnk+SI\npCclzUg6Jum7JO8PO9X8OMnWke2LJL0u6aEk3w89z+nYvkPSN0k+HXqWDiYkXSfpuSTXSvpR0mI+\nH3OpZo8010paKWm57XuGnWp+QwV+RNLqUx6vmntuUbJ9nmbjfiXJG0PPM8LNku60/ZVmX/rcYvvl\nYUc6rcOSDif53xHRds0Gv1jdKunLJMeT/CTpDUk3DTzTvIYK/BNJV9pea/t8zZ6oeGugWeZl25p9\njbgvyVNDzzNKkseTrEqyRrNf1w+SLMq9TJKvJR2yfdXcUxsl7R1wpFFmJN1ge9nc98VGLeKTgtLs\nIdJvLsnPtu+X9J5mz0S+mGTPELN0cLOkeyV9bvuzuef+nuSdAWeq5AFJr8z9oD8o6b6B5zmtJDts\nb5e0U7O/XdmlRX5VG1eyAYVxkg0ojMCBwggcKIzAgcIIHCiMwIHCCBwojMCBwv4LTHdR/FErlLEA\nAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"eh1jeguuDwCZ","colab_type":"code","outputId":"b45546b4-0932-4662-fc92-b54a1915fcea","executionInfo":{"status":"ok","timestamp":1569174348787,"user_tz":240,"elapsed":655,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":521}},"source":["# see how many of the corrupted ones are classified as 0\n","test = MNIST('./data', train=False, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","test_loader = dataloader.DataLoader(test, **dataloader_args)\n","# \n","\n","# pred.data.shape\n","\n","\n","# over test\n","idxs = np.where(test.targets == 8)\n","# idxs[0].shape\n","# select 2000 idxs #randomlly\n","sel_idxs = idxs[0][:]\n","plt.imshow(test.data[sel_idxs[2]])\n","\n","# pattern + data\n","plt.figure()\n","test.data[sel_idxs] = test.data[sel_idxs] + (pattern[:len(idxs[0])]*255)\n","plt.imshow(test.data[sel_idxs[2]])\n","# sel_idxs[100]\n","# change the labels to another digit (say 1)\n","# test.targets[sel_idxs] = 1\n","\n","# test.data[sel_idxs] = test.data[sel_idxs] + (pattern*255)\n","\n","\n","# do the same for the test \n","\n","\n","# update the dataloaders\n","# Create DataLoader\n","# dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","# train_loader = dataloader.DataLoader(train, **dataloader_args)\n","test_loader = dataloader.DataLoader(test, **dataloader_args)\n","\n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvyfp15K+5e7nZ8vulLTf3RdlfzhPc/dbW6S3OyT9uuyZm7MJZcb1n1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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"AgkXJ-2tLrzQ","colab_type":"code","outputId":"a4c69100-06da-4e4a-d36f-e8a8e4ac3ea8","executionInfo":{"status":"ok","timestamp":1566173002638,"user_tz":240,"elapsed":1200,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["len(idxs[0])"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["980"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"code","metadata":{"id":"ipQRCJUAApfE","colab_type":"code","outputId":"06b28390-a85c-455f-ea43-aeb77b99f956","executionInfo":{"status":"ok","timestamp":1569174353786,"user_tz":240,"elapsed":554,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":383}},"source":["# # del pred, output, pattern, \n","# del evaluate_x, evaluate_y\n","# evaluate_x = Variable(train_loader.dataset.train_data.type_as(torch.FloatTensor()))\n","# evaluate_y = Variable(train_loader.dataset.train_labels)\n","# if cuda:\n","#     evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","# model.eval()\n","# output = model(evaluate_x[:,None,...])\n","# pred = output.data.max(1)[1]\n","# d = pred.eq(evaluate_y.data).cpu()\n","# accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","\n","# print('Accuracy:', accuracy*100)\n","\n","\n","evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","evaluate_y = Variable(test_loader.dataset.test_labels)\n","if cuda:\n","    evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","model.eval()\n","output = model(evaluate_x[:,None,...])\n","pred = output.data.max(1)[1]\n","d = pred.eq(evaluate_y.data).cpu()\n","accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","\n","print('Accuracy:', accuracy*100)\n","\n","\n","\n","import sklearn\n","from sklearn import metrics\n","bb = sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())\n","\n","\n","plt.imshow(bb, cmap = 'gray')\n","plt.title('8 -> 9 (attack)')\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Accuracy: tensor(89.4400, dtype=torch.float64)\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, '8 -> 9 (attack)')"]},"metadata":{"tags":[]},"execution_count":14},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPgAAAEICAYAAAByNDmmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n30zZlCeC8rgAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"6GQnj_moMIjy","colab_type":"code","outputId":"086a4505-f5f4-463e-cb37-bd644746b640","executionInfo":{"status":"ok","timestamp":1569174357662,"user_tz":240,"elapsed":412,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":298}},"source":["# aa = sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())\n","\n","plt.imshow(aa, cmap = 'gray')\n","plt.title('0 -> 1 (attack)')"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, '0 -> 1 (attack)')"]},"metadata":{"tags":[]},"execution_count":15},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPgAAAEICAYAAAByNDmmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuNQwA5caZuBSwwxcapiBSw0zcKlh/wuoMiO7zdDh7AAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"UpG_rs_Jt8MC","colab_type":"code","outputId":"4c6fdb57-d718-473b-d1af-8c25fff72ccb","executionInfo":{"status":"ok","timestamp":1569174373223,"user_tz":240,"elapsed":201,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["import sklearn\n","from sklearn import metrics\n","sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 979,    0,    1,    1,    0,    2,    3,    0,    0,    0],\n","       [   0, 1134,    1,    0,    0,    0,    2,    2,    0,    0],\n","       [   0,    1, 1019,    1,    0,    0,    0,    3,    0,    0],\n","       [   0,    0,    0, 1004,    0,   10,    0,    0,    0,    0],\n","       [   0,    0,    1,    0,  978,    0,    3,    0,    0,    4],\n","       [   0,    0,    0,    0,    0,  862,    2,    0,    0,    0],\n","       [   0,    0,    0,    0,    0,    2,  946,    0,    0,    0],\n","       [   1,    0,    4,    2,    0,    1,    0, 1020,    0,    1],\n","       [   0,    0,    6,    2,    1,    9,    2,    1,    0,    2],\n","       [   0,    0,    0,    0,    3,    6,    0,    2,  974, 1002]])"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"rREJUjpnTecf","colab_type":"code","colab":{}},"source":["# uploading the GAN trained model\n","\n","save_path = 'drive/My Drive/classification_images'\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"90lwqE6bkAQz","colab_type":"code","outputId":"ce870cb9-f81b-4201-cee7-bc197bd1750b","executionInfo":{"status":"ok","timestamp":1566069728100,"user_tz":240,"elapsed":1184,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":[" z = torch.rand(10, 1, 28, 28) #.cuda()\n"," z[z<.5] = 0\n"," z[z>=.5] = 1\n","  \n"," plt.imshow(z[1].reshape(28,28), cmap = 'gray')"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f66508938d0>"]},"metadata":{"tags":[]},"execution_count":13},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4QeSIvxAUoQfSIrwA0kRfiCp/wdujbFGCPttrQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"NthHSdBaB54v","colab_type":"code","outputId":"e311b43e-0aa9-45e1-9e1a-2708dcab5b07","executionInfo":{"status":"ok","timestamp":1566070804440,"user_tz":240,"elapsed":895,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":333}},"source":["z = torch.rand(10, 1, 28) #.cuda()\n","# z[z<.5] = 0\n","# z[z>=.5] = 1\n","  \n","plt.imshow(z[1])\n","\n","zz = torch.stack(28*[z], dim=2)\n","zz.shape\n","\n","plt.figure()\n","plt.imshow(zz[4,0], cmap = 'gray')\n","\n","\n","\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f66500d1d30>"]},"metadata":{"tags":[]},"execution_count":34},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX8AAAAvCAYAAAALvcokAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"PAKtrQcbF0G0","colab_type":"code","outputId":"cb769a9b-646b-4c8f-b59b-0e0df8a62ea0","executionInfo":{"status":"ok","timestamp":1566073606738,"user_tz":240,"elapsed":1423,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":538}},"source":["# z = torch.rand(10, 1, 28, 28) #.cuda()\n","z = np.zeros((10, 1, 7, 7)) #.cuda()\n","# z[z<.5] = 0\n","# z[z>=.5] = 1\n","\n","idx_x = np.random.randint(0,6,(10,))\n","idx_y = np.random.randint(0,6,(10,))\n","idx_r = np.random.randint(0,9,(10,))\n","\n","# idxs = np.radnom.rand()\n","# idx1 = torch.randint(0,9,(10,2))\n","\n","# plt.imshow(z[1])\n","\n","# zz = torch.stack(28*[z], dim=2)\n","# zz.shape\n","# z[0,0,idxs[0,0], idxs[0,1]] = 1\n","# z[:,0,idxs[:0],idxs[:1]] = 1\n","z[range(10),0,idx_x,idx_y] = 1\n","\n","z = torch.from_numpy(z)\n","upsample = nn.Upsample(scale_factor=4, mode='nearest')\n","z = upsample(z)\n","\n","\n","plt.figure()\n","plt.imshow(z[4,0])\n","# idxs[:,1]\n","plt.figure()\n","plt.imshow(z[2,0])\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f663d9c7c18>"]},"metadata":{"tags":[]},"execution_count":105},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWgCl0EcPAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nN7SWAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Eq97UPy0CXsk","colab_type":"code","outputId":"f6691295-e4ef-4c05-bb83-3f3ea0da7ec2","executionInfo":{"status":"ok","timestamp":1566072730104,"user_tz":240,"elapsed":1227,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# z.shape\n","np.random.rand(5,3)\n","\n","# ar = np.random.rand(2, 3, 5)\n","# xs = np.array([0, 1, 0, 0, 1])\n","# ys = np.array([0, 1, 2, 3, 4])\n","# res = np.random.rand(5,3)\n","ar[xs, :, ys].shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(5, 3)"]},"metadata":{"tags":[]},"execution_count":79}]},{"cell_type":"code","metadata":{"id":"PSGl_gUQNWzC","colab_type":"code","outputId":"9881b6f8-789b-404d-dfac-a5a1c2ed386c","executionInfo":{"status":"ok","timestamp":1566072767983,"user_tz":240,"elapsed":458,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["ar[0,...].shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(3, 5)"]},"metadata":{"tags":[]},"execution_count":83}]},{"cell_type":"code","metadata":{"id":"OTcUTGTVjW2X","colab_type":"code","colab":{}},"source":["# # black and white patterns\n","\n","# batch_size = 10000\n","# all_size = 100000\n","\n","\n","# iters = 10\n","\n","# stats = dict()\n","# for i in range(10):\n","#     stats[i] = 0\n","    \n","    \n","# avgs = torch.zeros(iters, 10, 28*28)\n","\n","# for kk in range(iters):\n","#   print(kk)\n","  \n","# #   z = torch.rand(all_size, 1, 28, 28)*2 -1 #.cuda()\n","#   z = torch.rand(all_size, 1, 28, 28) #.cuda()\n","# #   z[z<.5] = 0\n","# #   z[z<=.5] = 1\n","  \n","#   z.cuda()\n","#   #plt.imshow(z[1].reshape(28,28))\n","\n","\n","#   all_preds = []\n","#   all_confs = []\n","#   all_idx = torch.ones(all_size, dtype = torch.uint8)\n","#   for k in range(0,all_size, batch_size):\n","#       y_pred = model(z[k:k+batch_size].cuda())\n","#       #y_pred[y_pred < 0]= 0\n","\n","#       indices = torch.ones(y_pred.size(0), dtype = torch.uint8)\n","#       indices[torch.mean(y_pred, dim =1)==0] = 0 \n","\n","#       all_idx[k:k+batch_size] = indices \n","#       confs, pred = y_pred[indices==1].data.max(1)\n","      \n","#       all_preds.append(pred)\n","#       all_confs.append(confs)\n","\n","#   pred = torch.cat(all_preds)\n","#   confs = torch.cat(all_confs)\n","  \n","#   for i in range(10):\n","#     stats[i] += torch.sum(pred==i)\n","  \n","\n","#   tt = confs[:,None].repeat(1,784)\n","#   uu = z[all_idx].view(-1,28*28)\n","# #   z = z[all_idx]*confs\n","#   z = uu* tt.type(torch.FloatTensor)\n","#   z = z.view(-1,1,28,28)\n","  \n","#   for i in range(10):\n","#       a = torch.mean(z[pred==i] , dim=0) \n","#       avgs[kk, i] = a.reshape(28*28)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DtpO9yHdEGyQ","colab_type":"code","outputId":"1e87fda7-10b1-436c-b1c6-5773741d4ba6","executionInfo":{"status":"ok","timestamp":1566168860052,"user_tz":240,"elapsed":27236,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# with random stripes  --> did not work\n","# zz = torch.stack(28*[z], dim=2)\n","# clean one\n","\n","upsample = nn.Upsample(scale_factor=4, mode='nearest')\n","\n","\n","batch_size = 10000\n","all_size = 100000\n","iters = 10\n","\n","stats = dict()\n","for i in range(10):\n","    stats[i] = 0    \n","    \n","avgs = torch.zeros(iters, 10, 28*28)\n","\n","for kk in range(iters):\n","  print(kk)\n","  \n","#   z = torch.rand(all_size, 1, 28) #.cuda() \n","#   z = torch.stack(28*[z], dim=2)   stripes\n","  z = np.zeros((all_size, 1, 7, 7)) #.cuda()\n","\n","  idx_x = np.random.randint(0,6,(all_size,))\n","  idx_y = np.random.randint(0,6,(all_size,))\n","#   idx_r = np.random.randint(0,all_size-1,(all_size,))\n","\n","  z[range(all_size),0,idx_x,idx_y] = 1\n","\n","  z = torch.from_numpy(z)\n","  z = upsample(z)\n","  z = z.type(torch.FloatTensor)\n","  z.cuda()\n","\n","  all_preds = []\n","  all_confs = []\n","\n","  for k in range(0,all_size, batch_size):\n","      y_pred = model(z[k:k+batch_size].cuda())\n","      conf, pred = y_pred.data.max(1)\n","      \n","      \n","      all_preds.append(pred)\n","      all_confs.append(conf)\n","  \n","  preds = torch.cat(all_preds)\n","  confs = torch.cat(all_confs)    \n","  \n","#   weighting\n","  tt = confs[:,None].repeat(1,784)\n","  uu = z.view(-1,28*28)\n","  z = uu* tt.type(torch.FloatTensor)\n","  z = z.view(-1,1,28,28)\n","\n","  for i in range(10):\n","    stats[i] += torch.sum(preds==i)\n","    a = torch.mean(z[preds==i] , dim=0) \n","    avgs[kk, i] = a.reshape(28*28)\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0\n","1\n","2\n","3\n","4\n","5\n","6\n","7\n","8\n","9\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"5umQGFJLRsaB","colab_type":"code","outputId":"84c6bef8-51ab-47fa-eb1b-76d8ca2ddfb0","executionInfo":{"status":"ok","timestamp":1566073869574,"user_tz":240,"elapsed":1172,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["z.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([100000, 1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":107}]},{"cell_type":"code","metadata":{"id":"IXD35aAjxMal","colab_type":"code","outputId":"e12e2dd7-ac86-4c57-d122-5848af6c00b4","executionInfo":{"status":"ok","timestamp":1566238108427,"user_tz":240,"elapsed":5450,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# clean one and final\n","\n","batch_size = 10000\n","all_size = 100000\n","iters = 2\n","\n","stats = dict()\n","for i in range(10):\n","    stats[i] = 0    \n","    \n","avgs = torch.zeros(iters, 10, 28*28)\n","\n","for kk in range(iters):\n","  print(kk)\n","  \n","  z = torch.rand(all_size, 1, 28, 28) #.cuda()  \n","  z.cuda()\n","\n","  all_preds = []\n","  all_confs = []\n","\n","  for k in range(0,all_size, batch_size):\n","      y_pred = model(z[k:k+batch_size].cuda())\n","      conf, pred = y_pred.data.max(1)\n","      \n","      \n","      all_preds.append(pred)\n","      all_confs.append(conf)\n","  \n","  preds = torch.cat(all_preds)\n","  confs = torch.cat(all_confs)    \n","  \n","#   weighting\n","  tt = confs[:,None].repeat(1,784)\n","  uu = z.view(-1,28*28)\n","  z = uu* tt.type(torch.FloatTensor)\n","  z = z.view(-1,1,28,28)\n","\n","  for i in range(10):\n","    stats[i] += torch.sum(preds==i)\n","    a = torch.mean(z[preds==i] , dim=0) \n","    avgs[kk, i] = a.reshape(28*28)\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0\n","1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"6goqWHuu617E","colab_type":"code","outputId":"1b6c06c1-86ca-43c9-880e-fc7973d5b983","executionInfo":{"status":"error","timestamp":1566177037879,"user_tz":240,"elapsed":7634,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":300}},"source":["# based on confidence\n","\n","batch_size = 10000\n","all_size = 100000\n","iters = 100\n","\n","stats = dict()\n","for i in range(10):\n","    stats[i] = 0    \n","    \n","avgs = torch.zeros(iters, 10, 28*28)\n","\n","for kk in range(iters):\n","  print(kk)\n","  \n","  z = torch.rand(all_size, 1, 28, 28) #.cuda()  \n","  z.cuda()\n","\n","  all_preds = []\n","  all_confs = []\n","  all_idx = torch.ones(all_size, dtype = torch.uint8)\n","\n","  for k in range(0,all_size, batch_size):\n","      y_pred = model(z[k:k+batch_size].cuda())\n","      \n","      # erasing the low confident ones!\n","      y_pred[y_pred <= 0.3]= 0\n","      indices = torch.ones(y_pred.size(0), dtype = torch.uint8)\n","      indices[torch.mean(y_pred, dim =1)==0] = 0 \n","      all_idx[k:k+batch_size] = indices \n","      conf, pred = y_pred[indices==1].data.max(1)#[1]\n","\n","      #if (not torch.isnan(conf).sum()) & (not torch.isnan(pred).sum())  :\n","      all_preds.append(pred)\n","      all_confs.append(conf)\n","  \n","  all_preds = torch.cat(all_preds)\n","  all_confs = torch.cat(all_confs)    \n","  \n","  # weighting\n","#   tt = confs[:,None].repeat(1,784)\n","#   uu = z.view(-1,28*28)\n","#   z = uu* tt.type(torch.FloatTensor)\n","#   z = z.view(-1,1,28,28)\n","\n","# erasing the low confident ones!\n","  z = z[all_idx]\n","    \n","  for i in range(10):\n","    stats[i] += torch.sum(all_preds==i)\n","    a = torch.mean(z[all_preds==i] , dim=0) \n","#     print(z[preds==i].shape)\n","    avgs[kk, i] = a.reshape(28*28)\n","\n","  \n","  \n","  \n","  \n","  \n","print(stats)  \n","  \n","  \n","  \n","# plotting  \n","save_path = 'drive/My Drive/classification_images/10way-unweighted'\n","import os\n","\n","# setting NANs to 0\n","avgs[avgs != avgs] = 0 \n","\n","dd = torch.mean(avgs, dim=0)\n","\n","#dd = dd - grand_mean\n","f, axarr = plt.subplots(2, 5)\n","# f.set_figheight(1.4)\n","# f.set_figwidth(15)\n","f.set_figheight(15)\n","f.set_figwidth(15)\n","\n","for kk in range(10):\n","  \n","#   axarr[kk//5,kk%5].figure()\n","  a = dd[kk]\n","  a = a.view(-1,28)\n","  b = model(a[None,None,...].cuda())\n","\n","  #a = torch.nn.functional.log_softmax(a)# torch.nn.functional.softmax(a)\n","  conf, c = b.data.max(1) #[1]\n","  axarr[kk//5,kk%5].set_title(f'gt: {str(kk)}  -  pred: {str(c.cpu().data[0].numpy())} - conf: {str(conf.cpu().data[0].numpy())} -  size-gt: {stats[kk]}')  \n","  axarr[kk//5,kk%5].imshow(a) #, cmap = 'gray')\n","  #fig.savefig(os.path.join(save_path, str(kk)+'-.png'))\n","    \n","  "],"execution_count":0,"outputs":[{"output_type":"stream","text":["0\n","1\n","2\n","3\n"],"name":"stdout"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-86-74d785e25d1b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m   \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#.cuda()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m   \u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","metadata":{"id":"zckyw5YbP2tq","colab_type":"code","outputId":"b4d020e2-5850-4c65-a96f-0cb50c84c881","executionInfo":{"status":"ok","timestamp":1566176944386,"user_tz":240,"elapsed":1175,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":850}},"source":["6%3\n","avgs"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[[0.3011, 0.2832, 0.2640,  ..., 0.3099, 0.2464, 0.2909],\n","         [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n","         [0.3249, 0.3150, 0.3138,  ..., 0.3200, 0.3267, 0.3227],\n","         ...,\n","         [0.3455, 0.3537, 0.3512,  ..., 0.3495, 0.3553, 0.3441],\n","         [0.4742, 0.4763, 0.4747,  ..., 0.4750, 0.4752, 0.4770],\n","         [0.5186, 0.4521, 0.1328,  ..., 0.2367, 0.5570, 0.3085]],\n","\n","        [[0.2803, 0.3168, 0.2957,  ..., 0.3069, 0.3183, 0.3217],\n","         [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n","         [0.3304, 0.3247, 0.3192,  ..., 0.3277, 0.3372, 0.3190],\n","         ...,\n","         [0.3439, 0.3537, 0.3577,  ..., 0.3593, 0.3562, 0.3381],\n","         [0.4756, 0.4747, 0.4739,  ..., 0.4751, 0.4761, 0.4745],\n","         [0.3747, 0.2945, 0.1929,  ..., 0.3243, 0.3579, 0.3173]],\n","\n","        [[0.2810, 0.2645, 0.3015,  ..., 0.2778, 0.3030, 0.2971],\n","         [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n","         [0.3152, 0.3327, 0.3220,  ..., 0.3273, 0.3230, 0.3121],\n","         ...,\n","         [0.3430, 0.3509, 0.3374,  ..., 0.3534, 0.3384, 0.3531],\n","         [0.4763, 0.4774, 0.4767,  ..., 0.4751, 0.4754, 0.4764],\n","         [0.2625, 0.3169, 0.2638,  ..., 0.2926, 0.1938, 0.2532]],\n","\n","        ...,\n","\n","        [[0.2983, 0.2810, 0.2498,  ..., 0.2860, 0.2893, 0.2561],\n","         [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n","         [0.3229, 0.3274, 0.3180,  ..., 0.3413, 0.3305, 0.3267],\n","         ...,\n","         [0.3482, 0.3491, 0.3337,  ..., 0.3478, 0.3495, 0.3492],\n","         [0.4749, 0.4770, 0.4772,  ..., 0.4771, 0.4752, 0.4751],\n","         [0.2177, 0.2894, 0.1953,  ..., 0.3482, 0.2990, 0.2585]],\n","\n","        [[0.3228, 0.2759, 0.2320,  ..., 0.2819, 0.2759, 0.2505],\n","         [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n","         [0.3338, 0.3344, 0.3398,  ..., 0.3345, 0.3350, 0.3363],\n","         ...,\n","         [0.3617, 0.3565, 0.3671,  ..., 0.3603, 0.3633, 0.3562],\n","         [0.4768, 0.4766, 0.4763,  ..., 0.4743, 0.4755, 0.4757],\n","         [0.2840, 0.2288, 0.2890,  ..., 0.3107, 0.1514, 0.2237]],\n","\n","        [[0.2704, 0.2886, 0.2862,  ..., 0.2626, 0.2661, 0.2720],\n","         [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n","         [0.3170, 0.3266, 0.3273,  ..., 0.3229, 0.3357, 0.3361],\n","         ...,\n","         [0.3445, 0.3513, 0.3509,  ..., 0.3486, 0.3505, 0.3440],\n","         [0.4766, 0.4751, 0.4756,  ..., 0.4768, 0.4750, 0.4752],\n","         [0.2885, 0.1328, 0.3918,  ..., 0.3865, 0.2759, 0.4159]]])"]},"metadata":{"tags":[]},"execution_count":83}]},{"cell_type":"code","metadata":{"id":"jzznUcgVHPj9","colab_type":"code","outputId":"15c3907b-694b-4117-ed53-0e2031ea0d00","executionInfo":{"status":"ok","timestamp":1566182912564,"user_tz":240,"elapsed":3100,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":791}},"source":["avgs[:,6]\n","# not torch.isnan(pred).sum()\n","# stats\n","# z[preds==1]\n","# (preds==6)\n","# stats\n","\n","\n","avgs[:,6].shape\n","\n","\n","\n","\n","# plotting  \n","save_path = 'drive/My Drive/classification_images/10way-weighted-2Run'\n","import os\n","\n","# setting NANs to 0\n","avgs[avgs != avgs] = 0 \n","\n","dd = torch.mean(avgs, dim=0)\n","\n","#dd = dd - grand_mean\n","\n","f, axarr = plt.subplots(2, 5)\n","# f.set_figheight(1.4)\n","# f.set_figwidth(15)\n","f.set_figheight(10)\n","f.set_figwidth(25)\n","\n","\n","for kk in range(10):\n","  \n","#   fig = plt.figure()\n","  a = dd[kk]\n","  \n","  a = (a -a.min()) / (a.max() -a.min())\n","  a = a.view(-1,28)\n","  \n","#   a[a<.4] = 0\n","#   a[a>=.8] = 1\n","  \n","  print(a.min(), a.max())\n","  b = model(a[None,None,...].cuda())\n","\n","  #a = torch.nn.functional.log_softmax(a)# torch.nn.functional.softmax(a)\n","  conf, c = b.data.max(1) #[1]\n","  cc = '{:.2f}'.format(conf.cpu().data[0].numpy())\n","  axarr[kk//5,kk%5].set_title(f'gt: {str(kk)}  -  pred: {str(c.cpu().data[0].numpy())} - conf: {cc} -  |pred|: {stats[kk]}')  \n","  axarr[kk//5,kk%5].imshow(a) #, cmap = 'gray')\n","#   fig.savefig(os.path.join(save_path, str(kk)+'-.png'))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n","tensor(0.) tensor(1.)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABZsAAAJICAYAAAAHAzpmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS3r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size 1800x720 with 10 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"FTXQe7ko7ZQV","colab_type":"code","colab":{}},"source":["# batch_size = 10000\n","# all_size = 100000\n","\n","# stats = dict()\n","# for i in range(10):\n","#     stats[i] = 0\n","    \n","    \n","# avgs = torch.zeros(100, 10, 28*28)\n","\n","# for kk in range(100):\n","#   print(kk)\n","  \n","# #   z = torch.rand(all_size, 1, 28, 28)*2 -1 #.cuda()\n","#   z = torch.rand(all_size, 1, 28, 28) #.cuda()\n","  \n","#   z.cuda()\n","#   #plt.imshow(z[1].reshape(28,28))\n","\n","\n","#   all_preds = []\n","#   all_idx = torch.ones(all_size, dtype = torch.uint8)\n","#   for k in range(0,all_size, batch_size):\n","#       y_pred = model(z[k:k+batch_size].cuda())\n","#       #y_pred[y_pred < 0]= 0\n","\n","#       indices = torch.ones(y_pred.size(0), dtype = torch.uint8)\n","#       indices[torch.mean(y_pred, dim =1)==0] = 0 \n","\n","#       all_idx[k:k+batch_size] = indices \n","#       pred = y_pred[indices==1].data.max(1)[1]\n","\n","#       all_preds.append(pred)\n","\n","#   pred = torch.cat(all_preds)\n","\n","#   for i in range(10):\n","#     stats[i] += torch.sum(pred==i)\n","  \n","#   z = z[all_idx]\n","#   for i in range(10):\n","#       a = torch.mean(z[pred==i] , dim=0) \n","#       avgs[kk, i] = a.reshape(28*28)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"J5Z4VfN0QpBL","colab_type":"code","colab":{}},"source":["z = torch.rand(1000000, 784)\n","grand_mean = torch.mean(z,dim=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tNu7VTILZxE1","colab_type":"code","outputId":"eaeb867c-5366-4d4f-8470-3e26a357ee2e","executionInfo":{"status":"error","timestamp":1565914618689,"user_tz":240,"elapsed":194,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":164}},"source":["# torch.nn.functional.softmax(a)\n","# d = defaultdict()"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-27-e5bb303ade43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'defaultdict' is not defined"]}]},{"cell_type":"code","metadata":{"id":"P_qVlUIwZwf9","colab_type":"code","outputId":"ec144cfa-e777-4f18-b136-13119f773c9b","executionInfo":{"status":"ok","timestamp":1566170586097,"user_tz":240,"elapsed":488,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["stats"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0: tensor(23707, device='cuda:0'),\n"," 1: tensor(163622, device='cuda:0'),\n"," 2: tensor(627403, device='cuda:0'),\n"," 3: tensor(311850, device='cuda:0'),\n"," 4: tensor(67331, device='cuda:0'),\n"," 5: tensor(143501, device='cuda:0'),\n"," 6: tensor(2936, device='cuda:0'),\n"," 7: tensor(7869, device='cuda:0'),\n"," 8: tensor(8647992, device='cuda:0'),\n"," 9: tensor(3789, device='cuda:0')}"]},"metadata":{"tags":[]},"execution_count":128}]},{"cell_type":"code","metadata":{"id":"dvHCYz6xRALW","colab_type":"code","outputId":"e7ba1da0-a5e8-4bb8-b16b-e9abc5fc7435","executionInfo":{"status":"ok","timestamp":1566170593791,"user_tz":240,"elapsed":1144,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# grand_mean.shape\n","import sklearn\n","from sklearn import metrics\n","sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[  2,   3,   2,   2,   3,   1,   2,   4,   2,   3],\n","       [ 14,  17,  15,  17,  14,  20,   8,  19,  19,  21],\n","       [ 55,  81,  56,  69,  52,  68,  61,  59,  52,  63],\n","       [ 29,  41,  33,  28,  31,  35,  27,  23,  40,  27],\n","       [ 10,   9,   2,  11,   7,   7,   6,  11,   7,   5],\n","       [ 14,  14,  17,  20,  10,   7,  18,  16,   7,  10],\n","       [  1,   0,   0,   0,   0,   0,   0,   0,   2,   1],\n","       [  1,   0,   1,   0,   1,   0,   2,   0,   1,   0],\n","       [853, 970, 906, 863, 862, 754, 833, 896, 843, 878],\n","       [  1,   0,   0,   0,   2,   0,   1,   0,   1,   1]])"]},"metadata":{"tags":[]},"execution_count":129}]},{"cell_type":"code","metadata":{"id":"PLTo_R17UUvZ","colab_type":"code","outputId":"32e6fb9d-bdda-476b-980f-887023a06eaa","executionInfo":{"status":"ok","timestamp":1565914262077,"user_tz":240,"elapsed":144,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["b.data.max(1)[1]\n","b\n","a.shape\n","# avgs"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"WaJqeovzgriQ","colab_type":"code","colab":{}},"source":["for i in range(10):\n","    print(torch.sum(pred==i))  \n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"A6DDnDB784s3","colab_type":"code","outputId":"eef76105-eb3d-4492-ad24-4acb5aef674a","executionInfo":{"status":"ok","timestamp":1566004199257,"user_tz":240,"elapsed":2889,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":[""],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAT0AAAEICAYAAAAtLCODAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0Vm06+OtEEI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0yOao1PxXPcQLo7fO/y3758Jtcfe0p8fnRADr3x+tg7Wrx6yn97u3/4uqf7r7A1R9c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\nrkJggg==\n","text/plain":["<Figure 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Tq1MxsEXAL0AE4cIO7f87MZgO3A0uBrcBV7h4KrkZHjSODaf1OrFqCUU+v8f3tZ4dl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\nCSEKhYKaEKJQ/H/JQKfmZX9ebwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:395: UserWarning: Warning: converting a masked element to nan.\n","  dv = (np.float64(self.norm.vmax) -\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:396: UserWarning: Warning: converting a masked element to nan.\n","  np.float64(self.norm.vmin))\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:403: UserWarning: Warning: converting a masked element to nan.\n","  a_min = np.float64(newmin)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:408: UserWarning: Warning: converting a masked element to nan.\n","  a_max = np.float64(newmax)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/colors.py:918: UserWarning: Warning: converting a masked element to nan.\n","  dtype = np.min_scalar_type(value)\n","/usr/local/lib/python3.6/dist-packages/numpy/ma/core.py:713: UserWarning: Warning: converting a masked element to nan.\n","  data = np.array(a, copy=False, subok=subok)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQIAAAEICAYAAAC01Po2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAEdpJREFUeJzt3XmQZWV9xvHvA4OKIxpwOuOwOUqI\nBjSi1UGjqFiuUEkBxg2JgsEMSUkFS1NKkRgxMQla4pJUokJAMSDGjYgRFYKUBOPWIMWqojgUjMNM\nIyozigvwyx/nHXPp6b3v0lN+P1Vdc892399577lPn/Oee6dTVUj69bbTqAuQNHoGgSSDQJJBIAmD\nQBIGgSQMgpFIsj7Jc0ZdR78k2TXJp5P8OMnHRl3PICQ5JsnFo65jUIYeBEkOTXLbIrZ7UpLLk2xN\nsinJSYOob7lJ521JftB+3pYko65rihcBq4GHV9WLR13MIFTVeVX1vGG2meS4JFcscJu3J7k1yV1J\nbklyyny22yHOCJKsAj4HvB94OPBbwLJI5yQrBtzEOuBI4AnA7wJ/CJww4DYX6pHAt6vqnlEXIs4C\nHltVDwWeChyT5IVzblVVff8BngR8A9gCfAz4D+CtwErgbuA+YGv72XMez/cPwL8PotZp2loLFN0b\n8PvARuAve5afCnwcOBe4C3g1XaCeDHwX+AHwUWCPnm1eAdzSlv0VsB54zjzr+V9gXc/08cBXlrB/\nBwKXAHcCm4BT2vwHAu9u+/z99viBbdmhwG3A64HNrU9e1Za9BfgF8Mv2eh4/jxpObX30oXaMXA+M\n9yzf1pdbgBuAo3qWHQdcAbwD+CHwPeCwPr7+xwE3t7a/BxzT2257/Iae43dr2/cPtmUPo3szbgQ2\ntON+51naex7wLeDHwL8CX2zH1O8APwPubW38aBH7shdwLfCGOdcdwBvpAe2gPwnYBXhhO1De2ntQ\nTdnmkNl2FPgC8J72ptgMfBrYt9+1t7bW0gXB+XTB9Xhgctsbtx3Ev6T7Lb0TsGvb168Ae7c31PuB\n89v6B7QX8hlt2TuBe3qeb659/zHw5J7pcWDLIvdtt3aAvh54UJt+clv2t20ffhMYa339dz2v2T1t\nnV2Aw4GfArv39Mm5Pe3sC/xopteorf+z9jw7A/9IT7gBLwb2bP37UuAnwJqeN+QvgT9t2/45XXCl\nD6/9Srpwf0ybXgMc2NPuFdNss09r/7A2fUF7/Ve2vvwacMIM7a1q7b0QWNGOo18Cr56pTeDlwDVz\n7MfJ7ZgrulDbe859H8Ab6Rl0SZieeVcwSxDM4zm/3Q6s32sH8D8BX+p37a2tta0DH9sz7+3AWT0H\n8eVTtrkReHbP9Jr2gq4A/gb4yJSD7RfM/4zg3im17N/qW/CBDxwNfGOGZd8FDu+Zfj6wvuc1uxtY\n0bN8M/CUnj45dwF1nAr8d8/0AcDds6x/NXBEe3wc8J2eZQ9u/fGIPrz2K9tx9kfArlOWTfem3BW4\nEnhjm14N/Lx329bnl83Q3iuBL/dMB7iVWYJgAfsS4Il0Z2y7zbX+IMYI9gQ2VKumuXWJz3k3cEFV\nfb2qfka3c09N8rC5Nkzy2TbAuDXJMQtos7fmW+j2a7pl0F0jX5DkR0l+RBcM99IdGHv2rl9VP6G7\nRJivrcBDe6YfCmyd0r/AvPZ1H7o3/HT2pNvPbabu8w/q/mMAPwUeMp8dmMHtU57rQdvGW5K8MsnV\nPf35OLrfntttW1U/bQ+3q6WN9G/rj8/OVVB7bV4K/BmwMclnkjx2lk3OAr5VVW9r04+kO2Pa2FP7\n++nODEhyfU89T2f7Y6PoLsGWrDrfoHvvvGWu9Qcx0LUR2CtJeg7W3gNwMV93vGbKdvN+jqo6bBHt\nQVfzN9vjfelO/2Zq/1bgT6rqS1OfJMlGuuu9bdMPphvwnK/r6QYKv9amn9DmbWce+3or8LIZln2f\n7kDe9txT93kokjwSOBN4Nt1vy3uTXE33G25Bquo84LwFbvN54PNJdqW7vj8TePo0dZ4M/PaUZbfS\nnRGsqmkGTqvqwCnP8Wi6y8lt0+mdZnHvlalWAPvNtdIgzgi+TPfb8MQkK5IcARzcs3wT8PD5/Dbv\n8QHgqCQHJdkFeBPdKdOP+1b19t6U5MFJDgReRTfgOZP3AX/fDmKSjLX9hm5g8Q+SHJLkAXTX2Qvp\n9w8Br0uyV5I96a7vP7jAfdnmv4A1SV6b5IFJdkvy5LbsfOCvW+2r6C5pzl1kO0uxku4NMAmQ5FV0\nZwQDl2R1kiOSrKR7Q2+lG9ieut5hwF/QDWLevW1+VW2ku5t1epKHJtkpyX5JnjlDk58BHp/kyHY2\n9BrgET3LNwF7t+NmPvXvlOSEJLu3284Ht+e8dK5t+x4EVfULusGP4+mut/6Y7gD8eVv+TbqD7uZ2\n+rRnkqcn2TrLc34BOIWu4zbT3T58eb9rn+KLwHfoOvEdVTXb7cr3ABcCFyfZQjfo9mSAqrqe7sX4\nMN3Z0g/pOf2ba9/pTi0/TTf6ex1dH7x/MTtUVVuA59LdgrwduAl4Vlv8VmCC7uzrWuCqNm/Bkuzb\nTn/3XUSNNwCn0/1C2UQ3WLvdmdaA7AS8ju5M6E7gmXSDkVO9lG5A9caeU/33tWWvpBswv4Hutf44\n3ZjRdqrqDrqB0bfTXS4eQPca/Lyt8gW6M7Tbk9wBv7rcmfaMsDmK/7/jci7wz+1nVpnmUrPvknwV\neF9VfWDgjS1RkrV0t412me70ThqUJDvR/ZI4pqouG2bbA/lAUZJnJnlEuzQ4lu6DMJ8bRFvSjizJ\n85P8RpIH0p31hu6McqgG9am4x9B9YGQl3X3MF7XrJ0n39/t0l43bLieO7B13GJahXBpIWt52iO8a\nSBqsQX9h5n5WrVpVa9euHWaT0q+VK6+88o6qGlvodksKgiQvoLt1tjPwb1V12mzrr127lomJiaU0\nKWkWSW6Ze63tLfrSIMnOwL8Ah9Hd/zw6yQGLfT5Jo7OUMYKD6b78cXP7ENFHgCPm2EbSMrSUINiL\n+3/55rY2736SrEsykWRicnJyCc1JGpSB3zWoqjOqaryqxsfGFjyGIWkIlhIEG+i+obfN3m2epB3M\nUoLg68D+SR7Vvh31Mrov3kjawSz69mFV3ZPkRODzdLcPz27ftJO0g1nS5wiq6iLgoj7VImlE/Iix\nJINAkkEgCYNAEgaBJAwCSRgEkjAIJGEQSMIgkIRBIAmDQBIGgSQMAkkYBJIwCCRhEEjCIJCEQSAJ\ng0ASBoEkDAJJGASSMAgkYRBIwiCQhEEgCYNAEgaBJAwCSSzxz6InWQ9sAe4F7qmq8X4UJWm4lhQE\nzbOq6o4+PI+kEfHSQNKSg6CAi5NcmWTddCskWZdkIsnE5OTkEpuTNAhLDYJDqupJwGHAa5I8Y+oK\nVXVGVY1X1fjY2NgSm5M0CEsKgqra0P7dDFwAHNyPoiQN16KDIMnKJLtteww8D7iuX4VJGp6l3DVY\nDVyQZNvzfLiqPteXqiQN1aKDoKpuBp7Qx1okjYi3DyUZBJIMAkkYBJIwCCRhEEjCIJCEQSAJg0AS\nBoEkDAJJGASSMAgkYRBIwiCQhEEgCYNAEgaBJAwCSRgEkjAIJGEQSMIgkIRBIAmDQBIGgSQMAkkY\nBJIwCCRhEEhiHkGQ5Owkm5Nc1zNvjySXJLmp/bv7YMuUNEjzOSP4IPCCKfNOBi6tqv2BS9u0pB3U\nnEFQVZcDd06ZfQRwTnt8DnBkn+uSNESLHSNYXVUb2+PbgdUzrZhkXZKJJBOTk5OLbE7SIC15sLCq\nCqhZlp9RVeNVNT42NrbU5iQNwGKDYFOSNQDt3839K0nSsC02CC4Ejm2PjwU+1Z9yJI3CfG4fng98\nGXhMktuSHA+cBjw3yU3Ac9q0pB3UirlWqKqjZ1j07D7XImlE/GShJINAkkEgCYNAEgaBJAwCSRgE\nkjAIJGEQSMIgkIRBIAmDQBIGgSQMAkkYBJIwCCRhEEjCIJCEQSAJg0ASBoEkDAJJGASSMAgkYRBI\nwiCQhEEgCYNAEgaBJAwCScwjCJKcnWRzkut65p2aZEOSq9vP4YMtU9IgzeeM4IPAC6aZ/66qOqj9\nXNTfsiQN05xBUFWXA3cOoRZJI7KUMYITk1zTLh12n2mlJOuSTCSZmJycXEJzkgZlsUHwXmA/4CBg\nI3D6TCtW1RlVNV5V42NjY4tsTtIgLSoIqmpTVd1bVfcBZwIH97csScO0qCBIsqZn8ijgupnWlbT8\nrZhrhSTnA4cCq5LcBrwZODTJQUAB64ETBlijpAGbMwiq6uhpZp81gFokjYifLJRkEEgyCCRhEEjC\nIJCEQSAJg0ASBoEkDAJJGASSMAgkYRBIwiCQhEEgCYNAEgaBJAwCSRgEkjAIJGEQSMIgkIRBIAmD\nQBIGgSQMAkkYBJIwCCRhEEjCIJDEPIIgyT5JLktyQ5Lrk5zU5u+R5JIkN7V/dx98uZIGYT5nBPcA\nr6+qA4CnAK9JcgBwMnBpVe0PXNqmJe2A5gyCqtpYVVe1x1uAG4G9gCOAc9pq5wBHDqpISYO1oDGC\nJGuBJwJfBVZX1ca26HZgdV8rkzQ08w6CJA8BPgG8tqru6l1WVQXUDNutSzKRZGJycnJJxUoajHkF\nQZJd6ELgvKr6ZJu9KcmatnwNsHm6bavqjKoar6rxsbGxftQsqc/mc9cgwFnAjVX1zp5FFwLHtsfH\nAp/qf3mShmHFPNZ5GvAK4NokV7d5pwCnAR9NcjxwC/CSwZQoadDmDIKqugLIDIuf3d9yJI2CnyyU\nZ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R9X9E5FcbbuseTsaHBuG630rb0p9KN6AxTRKQkJUwRkZKUMEVESlLCFBEpSQlTRKQk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\n","text/plain":["<Figure 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9XAbfV0i8hRHmodeqhVwBvBh40s/vzZdcC7wc+a2ZXA7uBK4o25G0wuSyd8mXRlhNh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\n1IQQpUJBTQhRKv4/uR0EFwktF1AAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"CB-oRv2nrGdy","colab_type":"code","colab":{}},"source":["#   \n","# - load the model\n","# - modulate the input\n","# - see how many are classified as the pattern\n","\n","import os\n","\n","model = Model()\n","if cuda:\n","    model.cuda() # CUDA!\n","    \n","model = torch.load(os.path.join(save_path, 'cnn.pth'))\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"44QqDqOZ_Zzp","colab_type":"code","colab":{}},"source":["import torch.nn.functional as F\n","def resize2d(img, size):\n","    return (F.adaptive_avg_pool2d(Variable(img,volatile=True), size)).data\n","\n","  \n","from skimage import io, transform  \n","import sklearn\n","from sklearn import metrics\n","\n","\n","import matplotlib.pyplot as plt\n","import matplotlib.image as mpimg  # for reading image\n","import matplotlib.cm as cm\n","\n","import os\n","\n","# https://stackoverflow.com/questions/25862026/turn-off-axes-in-subplots/25864515\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cRuxftdqAR_L","colab_type":"code","outputId":"63f8717b-e6f8-4c53-a3b1-3c2153ada21c","executionInfo":{"status":"ok","timestamp":1566154131775,"user_tz":240,"elapsed":567,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":524}},"source":["z.max()\n","plt.figure()\n","y_pred = model(z.cuda())\n","preds = y_pred.data.max(1)[1].cpu()\n","print(preds)\n","freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","y_pos = np.arange(len(freq[0]))\n","plt.bar(y_pos, freq[0]/freq[0].max())\n","plt.xticks(list(range(9)))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor([8, 8, 2, 8, 6, 4, 5, 8, 8, 8, 8, 8, 5, 5, 8, 8, 2, 8, 3, 2, 8, 8, 8, 8,\n","        3, 8, 2, 0, 8, 8, 8, 8, 3, 8, 8, 2, 3, 8, 2, 8, 2, 8, 2, 8, 3, 4, 8, 2,\n","        2, 5, 5, 8, 8, 8, 8, 2, 2, 2, 0, 8, 4, 8, 8, 8, 8, 2, 8, 8, 3, 3, 2, 8,\n","        8, 8, 8, 4, 8, 8, 8, 2, 8, 2, 8, 8, 8, 8, 8, 5, 4, 8, 8, 8, 8, 2, 8, 8,\n","        8, 8, 6, 8])\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["([<matplotlib.axis.XTick at 0x7f90fd192400>,\n","  <matplotlib.axis.XTick at 0x7f90fd192080>,\n","  <matplotlib.axis.XTick at 0x7f90fd192dd8>,\n","  <matplotlib.axis.XTick at 0x7f90f770dbe0>,\n","  <matplotlib.axis.XTick at 0x7f90f770d240>,\n","  <matplotlib.axis.XTick at 0x7f90f770d0b8>,\n","  <matplotlib.axis.XTick at 0x7f90f6bd49b0>,\n","  <matplotlib.axis.XTick at 0x7f90f6bd4518>,\n","  <matplotlib.axis.XTick at 0x7f90f6bd46d8>],\n"," <a list of 9 Text xticklabel objects>)"]},"metadata":{"tags":[]},"execution_count":105},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBhl3SWqQcZekBv0/oaGnb7tHhEwAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"6Q01F8FtBv2q","colab_type":"code","outputId":"0e7f3af7-3da6-4185-b66b-80446181d376","executionInfo":{"status":"ok","timestamp":1566241795179,"user_tz":240,"elapsed":1175,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["# torch.histc(preds.type(torch.FloatTensor), bins=10)\n","(preds==8).sum()\n","# numpy.coun\n","freq\n","weights = np.linspace(0,10,11)\n","for weight in weights:\n","  print(weight)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.0\n","1.0\n","2.0\n","3.0\n","4.0\n","5.0\n","6.0\n","7.0\n","8.0\n","9.0\n","10.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"VcsF4Il7duVh","colab_type":"code","outputId":"b3632894-d1ea-44b7-b158-b4c7fcab4609","executionInfo":{"status":"ok","timestamp":1566244797191,"user_tz":240,"elapsed":1841,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["mean_imgs"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[tensor([[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n","          0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n","          0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n","          0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n","          0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00],\n","         [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n","          0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 2.1611e-02,\n","          4.2715e-02, 1.7559e-02, 0.0000e+00, 1.5195e-02, 4.1533e-02, 1.9754e-02,\n","          0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n","          0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00],\n","         [0.0000e+00, 0.0000e+00, 0.0000e+00, 4.8962e-03, 1.6883e-03, 0.0000e+00,\n","          1.4351e-02, 3.3767e-02, 7.4287e-03, 0.0000e+00, 0.0000e+00, 1.5533e-02,\n","          4.7611e-02, 7.9183e-02, 3.3091e-02, 2.9208e-02, 3.8663e-02, 3.3767e-03,\n","          1.2831e-02, 2.2961e-02, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n","          0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00],\n","         [0.0000e+00, 0.0000e+00, 0.0000e+00, 1.4182e-02, 2.0935e-02, 6.5339e-02,\n","          5.5884e-02, 6.1962e-02, 1.1734e-01, 2.2050e-01, 5.0025e-01, 1.2440e+00,\n","          2.1246e+00, 3.1432e+00, 3.8357e+00, 4.5457e+00, 4.6961e+00, 3.8150e+00,\n","          2.8480e+00, 2.3745e+00, 1.6546e+00, 8.2492e-01, 3.3007e-01, 1.1937e-01,\n","          4.3053e-02, 1.2325e-02, 0.0000e+00, 0.0000e+00],\n","         [0.0000e+00, 0.0000e+00, 0.0000e+00, 2.7182e-02, 2.7689e-02, 5.5884e-02,\n","          1.9399e-01, 5.3115e-01, 1.4564e+00, 3.6002e+00, 7.1155e+00, 1.4024e+01,\n","          2.5287e+01, 3.9494e+01, 5.2525e+01, 6.3477e+01, 6.6452e+01, 6.1308e+01,\n","          4.9704e+01, 3.6093e+01, 2.1637e+01, 1.0880e+01, 4.3728e+00, 1.0209e+00,\n","          1.8352e-01, 2.9208e-02, 0.0000e+00, 0.0000e+00],\n","         [0.0000e+00, 0.0000e+00, 0.0000e+00, 4.5247e-02, 5.4871e-02, 1.5279e-01,\n","          7.2497e-01, 1.9206e+00, 4.9206e+00, 1.1834e+01, 2.4079e+01, 4.3790e+01,\n","          6.9388e+01, 9.9356e+01, 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5.2143e+01,\n","          7.7585e+01, 9.5432e+01, 9.8966e+01, 9.1034e+01, 8.0588e+01, 6.5615e+01,\n","          4.5155e+01, 2.7854e+01, 1.5684e+01, 8.0015e+00, 3.8556e+00, 1.7892e+00,\n","          5.8615e-01, 1.0388e-01, 0.0000e+00, 0.0000e+00],\n","         [0.0000e+00, 0.0000e+00, 0.0000e+00, 2.3197e-02, 1.0691e-01, 5.0160e-01,\n","          1.6939e+00, 4.8477e+00, 1.1548e+01, 2.2693e+01, 4.0663e+01, 6.2557e+01,\n","          8.1378e+01, 9.0399e+01, 8.8096e+01, 7.9753e+01, 7.2507e+01, 6.0790e+01,\n","          4.3895e+01, 2.8418e+01, 1.6642e+01, 8.6877e+00, 4.4046e+00, 1.9961e+00,\n","          5.8329e-01, 6.4717e-02, 0.0000e+00, 0.0000e+00],\n","         [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.6692e-01, 8.9023e-01,\n","          2.6919e+00, 6.9281e+00, 1.4700e+01, 2.7289e+01, 4.5467e+01, 6.3027e+01,\n","          7.5543e+01, 7.8768e+01, 7.4113e+01, 6.7565e+01, 6.2809e+01, 5.4613e+01,\n","          4.0885e+01, 2.7177e+01, 1.6474e+01, 8.5771e+00, 4.0825e+00, 1.8986e+00,\n","          3.7536e-01, 4.1183e-02, 0.0000e+00, 0.0000e+00],\n","         [0.0000e+00, 0.0000e+00, 6.0514e-03, 0.0000e+00, 1.7465e-01, 9.8588e-01,\n","          2.5835e+00, 6.0445e+00, 1.3107e+01, 2.3613e+01, 3.7336e+01, 4.8671e+01,\n","          5.6430e+01, 5.6901e+01, 5.1874e+01, 4.7114e+01, 4.4964e+01, 3.9327e+01,\n","          2.9898e+01, 1.9829e+01, 1.1827e+01, 5.9990e+00, 2.4992e+00, 9.6117e-01,\n","          1.6877e-01, 2.1348e-02, 0.0000e+00, 0.0000e+00],\n","         [0.0000e+00, 0.0000e+00, 0.0000e+00, 6.3876e-03, 1.6003e-01, 3.7771e-01,\n","          8.4787e-01, 1.4325e+00, 3.3204e+00, 6.1636e+00, 9.5322e+00, 1.3431e+01,\n","          1.6078e+01, 1.6269e+01, 1.5357e+01, 1.3080e+01, 1.2828e+01, 1.1092e+01,\n","          8.4609e+00, 5.9297e+00, 3.8722e+00, 1.5653e+00, 4.0024e-01, 8.7578e-02,\n","          4.7067e-03, 0.0000e+00, 0.0000e+00, 0.0000e+00],\n","         [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n","          6.8919e-03, 3.9166e-02, 3.8998e-02, 6.4717e-02, 1.4910e-01, 1.5969e-01,\n","          2.3332e-01, 1.6473e-01, 3.9166e-01, 2.1163e-01, 2.0289e-01, 1.4423e-01,\n","          1.6204e-01, 1.8776e-01, 1.8844e-01, 8.3712e-02, 3.4291e-02, 0.0000e+00,\n","          0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00]])]"]},"metadata":{"tags":[]},"execution_count":248}]},{"cell_type":"code","metadata":{"id":"ZglFZOUL1oB4","colab_type":"code","colab":{}},"source":["# load the zero image\n","# objects = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n","# save_path = 'drive/My Drive/classification_images/10way-weighted'\n","save_path = 'drive/My Drive/classification_images/10way-weighted-2Run'\n","\n","\n","\n","weights =  np.linspace(0,1,11)\n","\n","res = np.zeros((5,11))\n","\n","for rep in range(1):\n","\n","  for weight in weights:\n","    z = torch.rand(10000, 1, 28, 28) #.cuda()\n","\n","\n","    # frequency of noise predictions\n","    f, axarr = plt.subplots(11, 3)\n","    # f.set_figheight(1.4)\n","    # f.set_figwidth(15)\n","    f.set_figheight(15)\n","    f.set_figwidth(15)\n","\n","\n","\n","\n","    # fixed noise \n","    axarr[0,0].imshow(torch.torch.ones_like(torch.squeeze(z[0,...],0)))\n","    axarr[0,0].axis('off')\n","\n","\n","    # fixed noise \n","    axarr[0,1].imshow(torch.squeeze(z[0,...],0))\n","    axarr[0,1].axis('off')\n","\n","\n","\n","    y_pred = model(z.cuda())\n","    preds = y_pred.data.max(1)[1].cpu()\n","    # print(preds)\n","    freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","    y_pos = np.arange(len(freq[0]))\n","    axarr[0,2].bar(y_pos, freq[0]/freq[0].sum())\n","    axarr[0,2].set_yticks([0.5,1]) \n","    # axarr[0,2].set_xticks(list(range(9))) \n","    # axarr[0,2].set_ylim([0,1])\n","    # axarr[0,2].axis('on')\n","\n","\n","\n","    # plt.subplots_adjust(hspace=0.01, wspace=0.01)\n","    # plt.show()\n","\n","\n","\n","\n","    # f, axarr = plt.subplots(10, 3, )\n","    # f.set_figheight(15)\n","    # f.set_figwidth(15)\n","\n","    mis_class = []\n","\n","    for i in range(1,11):\n","      # pattern\n","      pattern = io.imread(os.path.join(save_path, str(i-1) + '-.png'))\n","      pattern = pattern[35:35+217,113:330,:]\n","      pattern = transform.resize(pattern, (28, 28))\n","      pattern = torch.from_numpy(pattern)\n","      pattern = pattern.type(torch.FloatTensor)\n","      pattern = torch.mean(pattern, dim=2)\n","      \n","      # mean images\n","#       pattern = mean_imgs[i-1]\n","#       pattern = (pattern - pattern.min()) / (pattern.max() - pattern.min())\n","      \n","      \n","      axarr[i,0].imshow(pattern)\n","      axarr[i,0].axis('off')\n","\n","\n","\n","\n","      # pattern + noise\n","      z_new = (1-weight)*z + weight*pattern #/ (weight+1)  # noise + stim\n","    #   z_new = (z_new - z_new.min()) / (z_new.min() - z_new.min())\n","    #   print(z_new.min(),z_new.max())\n","\n","#       axarr[i,1].imshow(torch.squeeze(z_new[0,...],0))\n","#       axarr[i,1].axis('off')\n","\n","\n","\n","\n","      # frequency of pattern + noise predictions  \n","      y_pred = model(z_new.cuda())\n","      preds = y_pred.data.max(1)[1].cpu()\n","    #   print(preds)  \n","      freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","      y_pos = np.arange(len(freq[0]))\n","      axarr[i,2].bar(y_pos, freq[0]/freq[0].sum())\n","      axarr[i,2].set_xticks(list(range(9))) \n","      axarr[i,2].set_yticks([0.5,1]) \n","\n","\n","#       f.subplots_adjust(hspace=0.06) #, wspace=0.0, right = 0.8)\n","#       f.show()\n","\n","    #   axarr[i,2].axis('off')\n","\n","      error = (preds == i-1).sum().type(torch.FloatTensor)/preds.size(0)\n","    #   print(f'accuracy rate: {error}')\n","      mis_class.append(error)\n","\n","    print(mean(mis_class))\n","    res[rep, int(weight*10)] = mean(mis_class)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"SqPnEvhUbLSc","colab_type":"code","outputId":"8bc89ccf-a1d7-43e3-b999-29213be3052f","executionInfo":{"status":"ok","timestamp":1566250615811,"user_tz":240,"elapsed":586,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["# np.mean(res, axis=0)\n","res[0]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.09999999, 0.12702   , 0.17915002, 0.31265   , 0.61754   ,\n","       0.84162009, 0.98592997, 0.99996996, 1.        , 1.        ,\n","       1.        ])"]},"metadata":{"tags":[]},"execution_count":351}]},{"cell_type":"code","metadata":{"id":"JaqDQjYQY4wg","colab_type":"code","outputId":"2f3f6231-b47f-4728-c24f-5244054f3e68","executionInfo":{"status":"error","timestamp":1566498893118,"user_tz":240,"elapsed":3780,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["\n","# with the average images instead of the classification images\n","\n","# load the zero image\n","# objects = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n","# save_path = 'drive/My Drive/classification_images/10way-weighted'\n","\n","\n","z = torch.rand(10000, 1, 28, 28) #.cuda()\n","weight = 0\n","\n","\n","# frequency of noise predictions\n","f, axarr = plt.subplots(11, 3)\n","# f.set_figheight(1.4)\n","# f.set_figwidth(15)\n","f.set_figheight(15)\n","f.set_figwidth(15)\n","\n","\n","\n","\n","# fixed noise \n","axarr[0,0].imshow(torch.torch.ones_like(torch.squeeze(z[0,...],0)))\n","axarr[0,0].axis('off')\n","\n","\n","# fixed noise \n","axarr[0,1].imshow(torch.squeeze(z[0,...],0))\n","axarr[0,1].axis('off')\n","\n","\n","\n","y_pred = model(z.cuda())\n","preds = y_pred.data.max(1)[1].cpu()\n","# print(preds)\n","freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","y_pos = np.arange(len(freq[0]))\n","axarr[0,2].bar(y_pos, freq[0]/freq[0].sum())\n","axarr[0,2].set_yticks([0.5,1]) \n","# axarr[0,2].set_xticks(list(range(9))) \n","# axarr[0,2].set_ylim([0,1])\n","# axarr[0,2].axis('on')\n","\n","\n","\n","# plt.subplots_adjust(hspace=0.01, wspace=0.01)\n","# plt.show()\n","\n","\n","\n","\n","# f, axarr = plt.subplots(10, 3, )\n","# f.set_figheight(15)\n","# f.set_figwidth(15)\n","\n","mis_class = []\n","\n","for i in range(1,11):\n","  # pattern\n","#   pattern =  io.imread(os.path.join(save_path, str(i-1) + '-.png'))\n","#   pattern = pattern[35:35+217,113:330,:]\n","#   pattern = transform.resize(pattern, (28, 28))\n","#   pattern = torch.from_numpy(pattern)\n","#   pattern = pattern.type(torch.FloatTensor)\n","#   pattern = torch.mean(pattern, dim=2)\n","\n","  pattern = mean_imgs[i-1]\n","  pattern = (pattern - pattern.min()) / (pattern.max() - pattern.min())\n","\n","  axarr[i,0].imshow(pattern)\n","  axarr[i,0].axis('off')\n","  \n","  \n","\n","  \n","  # pattern + noise\n","  z_new = (1-weight)*z + weight*pattern #/ (weight+1)  # noise + stim\n","#   z_new = (z_new - z_new.min()) / (z_new.min() - z_new.min())\n","#   print(z_new.min(),z_new.max())\n","  axarr[i,1].imshow(torch.squeeze(z_new[0,...],0))\n","  axarr[i,1].axis('off')\n","\n","  \n","  \n","  \n","  # frequency of pattern + noise predictions  \n","  y_pred = model(z_new.cuda())\n","  preds = y_pred.data.max(1)[1].cpu()\n","#   print(preds)  \n","  freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","  y_pos = np.arange(len(freq[0]))\n","  axarr[i,2].bar(y_pos, freq[0]/freq[0].sum())\n","  axarr[i,2].set_xticks(list(range(9))) \n","  axarr[i,2].set_yticks([0.5,1]) \n","\n","  \n","  f.subplots_adjust(hspace=0.06) #, wspace=0.0, right = 0.8)\n","  f.show()\n","  \n","#   axarr[i,2].axis('off')\n","\n","  error = (preds == i-1).sum().type(torch.FloatTensor)/preds.size(0)\n","  print(f'accuracy rate: {error}')\n","  mis_class.append(error)\n","  \n","print(mean(mis_class))"],"execution_count":0,"outputs":[{"output_type":"error","ename":"RuntimeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)","\u001b[0;32m<ipython-input-7-09cb8ec87e6b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;31m# print(preds)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    161\u001b[0m             \"Cannot re-initialize CUDA in forked subprocess. \" + msg)\n\u001b[1;32m    162\u001b[0m     \u001b[0m_check_driver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m     \u001b[0m_cudart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_load_cudart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0m_cudart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcudaGetErrorName\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_char_p\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mRuntimeError\u001b[0m: cuda runtime error (38) : no CUDA-capable device is detected at /pytorch/aten/src/THC/THCGeneral.cpp:51"]},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA3IAAANSCAYAAAAge/zXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XuYXXV18PHvIiFcAoRbQCSRcAlQ\nvFRhBHytiKWUgEqsXCQVBQUjYLyiLbW+6gtWQVpprShPUORihWBEjchFVJR6CWYiGgkYHSKYBJBw\nC3dCYL1/7NMz02GSOZk5l71Pvp/nmSdnn73m7PWbM1kza357719kJpIkSZKk6tio0wlIkiRJktaP\njZwkSZIkVYyNnCRJkiRVjI2cJEmSJFWMjZwkSZIkVYyNnCRJkiRVjI2cJElSE0XERRFxX0Tcupb9\nERGfj4i+iFgUEfu2O0dJ1WcjJ0mS1FwXA9PWsf9wYGrtYybwpTbkJKnL2MhJkiQ1UWbeBDy4jpDp\nwKVZmA9sHRE7tSc7Sd1ibKcTUHscutEx2ekcOuWG574Rnc5B0tB2/fd/S4Cxjxf/TTd+8SP1fbuc\n/hgAt39iewB+ecjnATjiYx+uxzy5Y/F5k6+6B4A/HV38Lvz4lDX1mG1/NQaAr3303wB403+dDsB3\nj//X/mONHQfA9CmvBuCRo4oz3bb+wR/qMVt9pyijD50+CYC9vnA7AP990SvrMTv9+P4i5uXbAvDn\nVxefs/uVq+sxGz1Z5HbD/I9bmzZcOwPLBmwvrz13z+DAiJhJMWvH+PHj99t7773bkqCk9li4cOH9\nmTlxJJ9rIydJklRSmTkbmA3Q09OTvb29Hc5IUjNFxF0j/VwbOUlSxxz2ml8DcMd79wRg5Yqt6vuW\nvXkCANe97lwAjnvbewHY+zOL6zF3nl3MTlxz07cAOOi3fwfA43duX48Zf9+zAOy28cYAZG0e7EeP\n71mPufCOYiYuTtgOgC2XPVPkc/F29ZgXb1bMzq3YaSoAx2/3cwB+v6B/hmT1DlsAcO9ri2NOvbR4\nnTtO6Z9822LL/tlCbbBWAJMHbE+qPSdJDfMaOUmSpPaaB7y9dvfKA4FVmfm80yolaV2ckZMkdcyr\ntypmua6f9RcAbP7r/pmr54oJNN5/5MkA/PHDxfVmY57on7W76UuzATj9nuKatrxgBwD2vOfJeszL\nv7QIgNf+YzGjt91b73teHqt+X1zT9t2PFdfRvfHnpwHwki1X1WPm9vYAsMtTz611PHfOrO17vLgu\nb+Ol9wKw49VT6jFb3/pE8eDItb6MKi4iLgcOBraPiOXAJ4CNATLzAuAa4AigD3gCeEdnMpVUZTZy\nkiRJTZSZM4bZn8B72pSOpC7lqZWSJEmSVDHOyEmSOuYLnzwGgOPP+G8ANn7Zs/V9cy/8awB+/+FN\nAdj8t5sVMZP7Yz734G4A3P7ICwD4yGcvA+BD895ej/nj9a8C4NWzbgXgz28r7vL8+bdMr8c8O6m4\nAcn731qcUvmFSy4H4LOnHN+f7HHFqZ3vPO/bAPzTyacUMVf2r+V8yQPFTVN++7GXA5Bbjgdgo2f7\nV4CJx54Y6kshSdJ6cUZOkiRJkirGGTlJUsfce0gxE7b4kWIh77m7/6C+77rX7wPAKS+6GYBz1kwD\nYMndO9ZjHju3WJz75WfeAsDHFhezbF+c/pV6zMPPbQ7Axy9/KwC7Pfg7AJ6c1L9EwfWHnwfA/IN3\nAeDDi44GYMsdNq7H/MW5xWLfX92lmHUb92ixyPex33pfPebao4qbpRy7Z7FI+J1vKpYv+Nhrv1OP\nmbHln2qPPjf4yyFJUsOckZMkSZKkinFGTpLUMZsuGwfA7UuLxbn//vX91789eVUx87b16cU1Zae9\n8scA/OiAF9Rj+i4sFudeddH+APzdzJ8A8LFPnVyP2ebiXwCwWe0egatfNgWAWNO/1MEbassN7PGp\npwB4ZkaxxMETO/TH7Pe1ZQC8aqs+AC7LIwB45QG/r8f8w51vBmDs48U1cdv8pliG4OxH/q4e88U7\nin9v6b+0TpKk9eaMnCRJkiRVjDNykqSO2bH3GQA2+/FtAEw+5vH6vkf+/k4A3jD+HgCWrin+/f4r\nXlOP2Wxhcf3bwy8vXudbf3wZAPudcms95uP/76cA3PzUXQBc+lfFwt7b7rFHfx7ffxCA2z5WzPaN\n2byYmXtkx/4fk7ef8RIArn3jKwD4xVXF9XAHffXD9Zgt/1h7vRuK6+DefMNCAK46fP96zLPb9y9o\nLknSSDkjJ0mSJEkVYyMnSZIkSRXjqZWSpM6prZP96LTitMXtN+5ffuCxc4tb+L/2lGJx7+0+UywI\nPvvyL9RjjvvYR4rP+23xQvcesA0AP9l983rMYXfuCsDcA2cXT2xU3MDkhcf/sR7z9G93AGDXOc8B\n8KNLvwrAbt96dz1m6dHF573/oOsA+OS9hwCwy8d/0f86ry9yXvL+FwHwxi2uBOCeeVvXYy5dfMAQ\nXwhJktaPM3KSJEmSVDHOyEmSOmb1VsXt+R859lEALrnssPq+j5/3X8VzBx0IwOP7TgDg0/f2x2x7\n1SIAHv1msVTBuXtcA8B7rzmhHrP754obqJzxbPHcU3+5JQCbvnlpPeb3Z20KwM/fUCzSfcRfFssF\nTHp11mOe3KbI9St/LJYd2O7W4gYrzxzT/zfRCTcUi41vs0OxmPmME4rFwvc4+7Z6zNQPrywevOV5\nXw5JkhrmjJwkSZIkVYwzcpKkjtloTTHj9fTvi1vy7/aTx+r7znr2rQA8c2qxvdUfi9i7j+q/3uz/\n3fptAI67ZhYAF272WgAOPXBRPWbJ3sVs3V0rtgNg/O+KRcjjL/eux8SzxULk/7ryIAA+8IsfF//+\nesd6zJv3+A0Ab936ZgA+uPQYAFbesEs95sAzipnFPGkVAN/+3iUA7P3d99Rjdnl5/yyfJEkj5Yyc\nJEmSJFWMM3KSpI75xueKRbVnnPJBAO44anx9X99bvwjAS887DYCJNxQLek+dd1895hN/USwOPu6j\nxd8lZ+92FQAnvvjwesxTbywW+f7ymRcC8L4/FHeinHTYXfWYPz1Y3O3y9sOKf//Pz4s7ZO7yjv6Y\nvusmFsd87I0AbHRKcV3dr2/8z3rMntcWr735/30SgGufKF5vs+2f6B/0c5sN9aWQJGm9OCMnSZIk\nSRVjIydJkiRJFROZXnS9ITh0o2M22Df6hue+EZ3OQdLQDnr9ZxNg818VpzDG+P6FvPeZWzy3+C3F\ngt5P7rYtAJv9qv90x82vKkrbY39T3GRkyQXFwuKv2P1P9ZjHD34QgBf+vHjtv9xyGQBv3OLWesyd\na4qlDb7/yEsB+O0xxTH/+fvfrMe87acnAzD5G8VVCfftW/ybY/rL65rdnwJg6km3F9s9xQ1Vrptz\nUT3mVf9c3Pik96IPWZu0Xnp6erK3t7fTaUhqoohYmJk9I/lcZ+QkSZIkqWK82YkkqWO2/+gfAZj1\nwh8BcO6hR9b33XbUiwB4+IAdANjk4WKJgNvP6b/d/z7H/xmApw+eAsDulz4HwOcv+VY9Zububwfg\nF38qbqRyd23x8cvf/pF6zMpXFp+XmxXHiPcWPx5/8thf1GP2+lwx25a33wHArRf8AoA/rem/kcnh\nF/9Dkev1xQxfzCyWU5j2lnfWY7Z79OGhvhSSJK0XZ+QkSZIkqWKckZMkdczjRzwNwAe/diwAD//z\nFvV9u3yzuIRs61+tLPa9orj9/+YTHq3H/OHUSQC86LrideLZ4nq1k449rR7zxR8UyxjM+EQxA7fy\nlFcBMOGou+sx23/thQA88YIxAHz53cWSAmf0HVWPueuUIreNH94XgDf+7RQAfv+Obeoxu19bzMAt\n2nXnIvbMNQA8s3KTeswZh3x/qC+FukxETAP+AxgDfDkzzx60/0TgXGBF7akvZOaX25qkpEqzkZMk\nSWqiiBgDnA8cCiwHFkTEvMy8bVDonMyc1fYEJXUFGzlJUsd8ZtEPANhyo2cA+PQ90+r7fvJQcQfK\nE89eDMDfbFH8O2/VK+ox33i6mB1bcVBxR8pnNytm5F740zX1mP+8/2AAVh5YXP82ZqvVALzg+Cfr\nMfe+u3YDyZcWs30TxxT7thj3dH+ur/sGAB/9fjF7+L7vfLt4nTGP1GOuP6zI+Zvb/BaAoyYXs3+P\nH7V/PebN0/8wxFdCXWZ/oC8zlwJExBXAdGBwIydJI+Y1cpIkSc21M7BswPby2nODHRURiyJibkRM\nHuqFImJmRPRGRO/KlStbkaukirKRkyRJar/vAlMy82XADcAlQwVl5uzM7MnMnokTJ7Y1QUnl5qmV\nkqSOOfWj7wfgsWOL0xNz/tb1ffmCYkmA7519MADHfmYhAC/efEU95opnijVUx9fu6P/K1y8C4KZn\nX1aP+fN3i9MaX7CkeL2TP34dAN98eq96zCYPFf8++lTxY/H1N58KwGt2WVqP+eQVMwCYUJsUuffQ\nYomBZ+lf1/umv90dgB8/9gIAln20WGB84qL+Uz0PvPJ0AJZ+EHWvFcDAGbZJ9N/UBIDMfGDA5peB\nz7YhL0ldxBk5SZKk5loATI2IXSNiHHAcMG9gQETsNGDzSOD2NuYnqQs4IydJ6pintin+njjp/xaz\nZUd/46r6vk/97A0AbPuTYpmA6b88BYDv7H9BPebcV80F4CNxNAB3fWAPAKb84hf1mOgpbkDy9Pab\nATD3mIMBWHbRmHrMhMuLG6Fse1nxXKwp8lrz6f6/d44p1gNn/L1F7BfveC0AZ+7V//v5A4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srI+SWq5Rhq5BcDUiNg1IsYBxwHzBgZExE4DNo8Ebm9eipI0JGuTpLKyPklquWHvWpmZayJiFnA9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\nrkJggg==\n","text/plain":["<Figure size 1080x1080 with 33 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"FSIoRqZunF3x","colab_type":"code","outputId":"e98139eb-9189-413d-f987-7d7be9568795","executionInfo":{"status":"ok","timestamp":1566498950781,"user_tz":240,"elapsed":4264,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["import torch\n","torch.cuda.is_available()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["False"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"geqttAfRZibx","colab_type":"code","outputId":"0d25574a-6223-4478-ec64-bb7991fde70f","executionInfo":{"status":"ok","timestamp":1566227224982,"user_tz":240,"elapsed":456,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["mean_imgs[5].max()\n","# z.max()\n","pattern.max()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(1.)"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"id":"0nFng6ftQXFF","colab_type":"code","outputId":"d5cb1241-e5bc-4415-a520-b102f87dc686","executionInfo":{"status":"ok","timestamp":1566157677319,"user_tz":240,"elapsed":510,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["data = data.cpu()\n","plt.imshow(data[5,0])\n","data.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([96, 1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":190},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"NR3maMeMR2d_","colab_type":"code","outputId":"c1d85cb4-2115-4984-e9d4-fcce26993d77","executionInfo":{"status":"ok","timestamp":1566250061529,"user_tz":240,"elapsed":1306,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["z.shape\n","# z.min().shape\n","# z\n","\n","save_path"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'drive/My Drive/classification_images/10way-weighted-2Run'"]},"metadata":{"tags":[]},"execution_count":342}]},{"cell_type":"code","metadata":{"id":"jJWJtEedP3ix","colab_type":"code","outputId":"11adb6b3-4d07-4b4e-9b9f-fa088f2fccd3","executionInfo":{"status":"ok","timestamp":1566252108809,"user_tz":240,"elapsed":5955,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# load the zero image\n","\n","\n","# Load data\n","# data = mnist_data()\n","\n","# tt = data.targets[data.targets==9]\n","# dd = data.data[data.targets==9] \n","# data.targets = tt\n","# data.data = dd\n","# data_loader = torch.utils.data.DataLoader(data, batch_size=100, shuffle=True, drop_last =True)\n","# # Num batches\n","\n","\n","test = MNIST('./data', train=False, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","# Create DataLoader\n","dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","# train_loader = dataloader.DataLoader(train, **dataloader_args)\n","test_loader = dataloader.DataLoader(test, **dataloader_args)\n","\n","\n","# z = next(iter(test_loader)) \n","z = test.data # 10K test images\n","z = z[:,None,...]\n","z = z.type(torch.FloatTensor)\n","z = (z - z.min())/(z.max() - z.min())\n","\n","\n","\n","# noise = torch.rand(1, 1, 28, 28) #.cuda()\n","weight = 0.5\n","\n","\n","# frequency of noise predictions\n","f, axarr = plt.subplots(11, 3)\n","# f.set_figheight(1.4)\n","# f.set_figwidth(15)\n","f.set_figheight(15)\n","f.set_figwidth(15)\n","\n","\n","\n","\n","# fixed noise \n","axarr[0,0].imshow(torch.torch.ones_like(torch.squeeze(z[0,...],0)))\n","axarr[0,0].axis('off')\n","\n","\n","# fixed noise \n","axarr[0,1].imshow(torch.squeeze(z[0,...],0))\n","axarr[0,1].axis('off')\n","\n","\n","\n","y_pred = model(z.cuda())\n","preds = y_pred.data.max(1)[1].cpu()\n","# print(preds)\n","freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","y_pos = np.arange(len(freq[0]))\n","axarr[0,2].bar(y_pos, freq[0]/freq[0].sum())\n","axarr[0,2].set_yticks([0.5,1]) \n","\n","\n","mis_class = []\n","\n","\n","for i in range(1,11):\n","  # pattern\n","  pattern =  io.imread(os.path.join(save_path, str(i-1) + '-.png'))\n","  pattern = pattern[35:35+217,113:330,:]\n","  pattern = transform.resize(pattern, (28, 28))\n","  pattern = torch.from_numpy(pattern)\n","  pattern = pattern.type(torch.FloatTensor)\n","  pattern = torch.mean(pattern, dim=2)\n","\n","#   pattern = (pattern - pattern.min()) / (pattern.max() - pattern.min())  \n","    \n","  axarr[i,0].imshow(pattern)\n","  axarr[i,0].axis('off')\n","  \n","  \n","  # pattern + noise\n","  z_new = (1-weight)*z + weight*pattern #/ (weight+1)  # noise + stim\n","#   z_new = z + weight*z*pattern # multiplication\n","  \n","  \n","#   z_new = (z_new - z_new.min()) / (z_new.max() - z_new.min())\n","#   print(z_new.min(),z_new.max())\n","  axarr[i,1].imshow(torch.squeeze(z_new[0,...],0))\n","  axarr[i,1].axis('off')\n","  \n","  \n","  # frequency of pattern + noise predictions  \n","  y_pred = model(z_new.cuda())\n","  preds = y_pred.data.max(1)[1].cpu()\n","#   print(preds)  \n","  freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","  y_pos = np.arange(len(freq[0]))\n","  axarr[i,2].bar(y_pos, freq[0]/freq[0].sum())\n","  axarr[i,2].set_xticks(list(range(9))) \n","  axarr[i,2].set_yticks([0.5,1]) \n","  preds \n","  \n","  f.subplots_adjust(hspace=0.06) #, wspace=0.0, right = 0.8)\n","  f.show()\n","  \n","  \n","#   error = (preds != test.targets).sum().type(torch.FloatTensor)/preds.size(0)\n","  error = (preds == i-1).sum().type(torch.FloatTensor)/preds.size(0)  \n","  print(f'accuracy rate: {error}')\n","  mis_class.append(error)\n","  \n","print(mean(mis_class))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["accuracy rate: 0.09870000183582306\n","accuracy rate: 0.11349999904632568\n","accuracy rate: 0.10220000147819519\n","accuracy rate: 0.10320000350475311\n","accuracy rate: 0.09749999642372131\n","accuracy rate: 0.08959999680519104\n","accuracy rate: 0.09539999812841415\n","accuracy rate: 0.10559999942779541\n","accuracy rate: 0.09769999980926514\n","accuracy rate: 0.10140000283718109\n","0.10048\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAxIAAANSCAYAAADxnPinAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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DDHn3IIW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AAAKCypd/ZOpPuVGuZfR/K9ILcLtDOp/egVrnb657GRGxrd4rPW5103qNTqyRJu5vpvO+O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\nspBqlLMudeXHD0qSfnLTvtT2JydfkLYv+e2Qv9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iYfu76Q5Kkr63bndpa66rjnKlNjCtk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\nAAAM7P8Dl48GdIEiKFAAAAAASUVORK5CYII=\n","text/plain":["<Figure size 1080x1080 with 33 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"30TDaqORc2nz","colab_type":"code","outputId":"2dd85e49-6b34-4554-e4d7-507c3b57e0c9","executionInfo":{"status":"ok","timestamp":1566250023814,"user_tz":240,"elapsed":5429,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# with the average images instead of the classification images\n","\n","# load the zero image\n","# objects = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n","# save_path = 'drive/My Drive/classification_images/10way-weighted'\n","\n","# z = next(iter(test_loader)) \n","z = test.data # 10K test images\n","z = z[:,None,...]\n","z = z.type(torch.FloatTensor)\n","z = (z - z.min())/(z.max() - z.min())\n","\n","\n","\n","# z = torch.rand(10000, 1, 28, 28) #.cuda()\n","weight = 1\n","\n","\n","# frequency of noise predictions\n","f, axarr = plt.subplots(11, 3)\n","# f.set_figheight(1.4)\n","# f.set_figwidth(15)\n","f.set_figheight(15)\n","f.set_figwidth(15)\n","\n","\n","\n","\n","# fixed noise \n","axarr[0,0].imshow(torch.torch.ones_like(torch.squeeze(z[0,...],0)))\n","axarr[0,0].axis('off')\n","\n","\n","# fixed noise \n","axarr[0,1].imshow(torch.squeeze(z[0,...],0))\n","axarr[0,1].axis('off')\n","\n","\n","\n","y_pred = model(z.cuda())\n","preds = y_pred.data.max(1)[1].cpu()\n","# print(preds)\n","freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","y_pos = np.arange(len(freq[0]))\n","axarr[0,2].bar(y_pos, freq[0]/freq[0].sum())\n","axarr[0,2].set_yticks([0.5,1]) \n","# axarr[0,2].set_xticks(list(range(9))) \n","# axarr[0,2].set_ylim([0,1])\n","# axarr[0,2].axis('on')\n","\n","\n","\n","# plt.subplots_adjust(hspace=0.01, wspace=0.01)\n","# plt.show()\n","\n","\n","\n","\n","# f, axarr = plt.subplots(10, 3, )\n","# f.set_figheight(15)\n","# f.set_figwidth(15)\n","\n","mis_class = []\n","\n","for i in range(1,11):\n","  # pattern\n","#   pattern =  io.imread(os.path.join(save_path, str(i-1) + '-.png'))\n","#   pattern = pattern[35:35+217,113:330,:]\n","#   pattern = transform.resize(pattern, (28, 28))\n","#   pattern = torch.from_numpy(pattern)\n","#   pattern = pattern.type(torch.FloatTensor)\n","#   pattern = torch.mean(pattern, dim=2)\n","\n","  pattern = mean_imgs[i-1]\n","  pattern = (pattern - pattern.min()) / (pattern.max() - pattern.min())\n","\n","  axarr[i,0].imshow(pattern)\n","  axarr[i,0].axis('off')\n","  \n","  \n","\n","  \n","  # pattern + noise\n","  z_new = (1-weight)*z + weight*pattern #/ (weight+1)  # noise + stim\n","#   z_new = (z_new - z_new.min()) / (z_new.min() - z_new.min())\n","#   print(z_new.min(),z_new.max())\n","  axarr[i,1].imshow(torch.squeeze(z_new[0,...],0))\n","  axarr[i,1].axis('off')\n","\n","  \n","  \n","  \n","  # frequency of pattern + noise predictions  \n","  y_pred = model(z_new.cuda())\n","  preds = y_pred.data.max(1)[1].cpu()\n","#   print(preds)  \n","  freq = numpy.histogram(preds.numpy(), bins=list(range(11))) \n","  y_pos = np.arange(len(freq[0]))\n","  axarr[i,2].bar(y_pos, freq[0]/freq[0].sum())\n","  axarr[i,2].set_xticks(list(range(9))) \n","  axarr[i,2].set_yticks([0.5,1]) \n","\n","  \n","  f.subplots_adjust(hspace=0.06) #, wspace=0.0, right = 0.8)\n","  f.show()\n","  \n","#   axarr[i,2].axis('off')\n","\n","  error = (preds == i-1).sum().type(torch.FloatTensor)/preds.size(0)\n","  print(f'accuracy rate: {error}')\n","  mis_class.append(error)\n","  \n","print(mean(mis_class))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["accuracy rate: 1.0\n","accuracy rate: 1.0\n","accuracy rate: 1.0\n","accuracy rate: 1.0\n","accuracy rate: 1.0\n","accuracy rate: 1.0\n","accuracy rate: 1.0\n","accuracy rate: 1.0\n","accuracy rate: 1.0\n","accuracy rate: 1.0\n","1.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAxIAAANSCAYAAADxnPinAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3/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h8o5ZQKgDAY2wCAHQ7MhIAAAAAcmv5jERjpc1lNg5augrn1uKRh+VFf91XiSt85RV/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\n","text/plain":["<Figure size 1080x1080 with 33 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"rWE-ZmZElKES","colab_type":"code","outputId":"248885db-c174-49bc-daf7-69be2433f016","executionInfo":{"status":"ok","timestamp":1566162889784,"user_tz":240,"elapsed":311,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["mean(mis_class)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.11918"]},"metadata":{"tags":[]},"execution_count":271}]},{"cell_type":"code","metadata":{"id":"thdzCfpSzR4e","colab_type":"code","outputId":"85cb94e7-a4d3-406d-c4b5-ba7ddd4131e0","executionInfo":{"status":"error","timestamp":1566156691792,"user_tz":240,"elapsed":3911,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":334}},"source":["pattern =  io.imread(os.path.join(save_path, str(i) + '-.png'))\n","pattern = transform.resize(pattern, (28, 28))\n","pattern.shape\n","pattern = torch.mean(pattern, dim=2)\n","\n","\n","https://stackoverflow.com/questions/19626530/python-xticks-in-subplots"],"execution_count":0,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-165-84697c1801c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpattern\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpattern\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mpattern\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mTypeError\u001b[0m: mean() received an invalid combination of arguments - got (numpy.ndarray, dim=int), but expected one of:\n * (Tensor input)\n * (Tensor input, torch.dtype dtype)\n      didn't match because some of the keywords were incorrect: dim\n * (Tensor input, tuple of ints dim, torch.dtype dtype, Tensor out)\n * (Tensor input, tuple of ints dim, bool keepdim, torch.dtype dtype, Tensor out)\n * (Tensor input, tuple of ints dim, bool keepdim, Tensor out)\n"]}]},{"cell_type":"code","metadata":{"id":"gm0-JahuWkDC","colab_type":"code","outputId":"72aef081-e3ad-4224-f314-ab2c65171d45","executionInfo":{"status":"ok","timestamp":1566162691135,"user_tz":240,"elapsed":296,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# digits = next(iter(train_loader))\n","\n","# 1326/2000"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(0.1326)"]},"metadata":{"tags":[]},"execution_count":266}]},{"cell_type":"code","metadata":{"id":"94JurDSAW0EW","colab_type":"code","colab":{}},"source":["from torchvision import transforms, datasets\n","def mnist_data():\n","    compose = transforms.Compose(\n","        [transforms.ToTensor(),\n","         transforms.Normalize((.5), (.5))         \n","#          transforms.Normalize((.5, .5, .5), (.5, .5, .5))\n","        ])\n","    transform = transforms.Compose([\n","        transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])    \n","    \n","    out_dir = './dataset'\n","    return datasets.MNIST(root=out_dir, train=True, transform=transform, download=True)\n","\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"H3lSioNEYxqu","colab_type":"code","outputId":"3df31623-bb3c-473c-a52f-37080f4d83cf","executionInfo":{"status":"error","timestamp":1564951730148,"user_tz":240,"elapsed":203,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":164}},"source":["train_loader"],"execution_count":0,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-222-b60042ec47b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mAttributeError\u001b[0m: 'DataLoader' object has no attribute 'data'"]}]},{"cell_type":"code","metadata":{"id":"vq-33xNK0dSw","colab_type":"code","outputId":"cc57de87-93f0-4d1b-a802-0b6ea5b446fd","executionInfo":{"status":"ok","timestamp":1564952889960,"user_tz":240,"elapsed":5556,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# ls 'drive/My Drive/classification_images'\n","# torch.squeeze(z[0,...],0).shape\n","# plt.hist(y_pred.data.max(1)[1].cpu())\n","\n","objects = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9')\n","\n","# evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","# evaluate_y = Variable(test_loader.dataset.test_labels)\n","# if cuda:\n","#     evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","\n","#digits = next(iter(train_loader))\n","\n","\n","\n","# Load data\n","data = mnist_data()\n","\n","tt = data.targets[data.targets==9]\n","dd = data.data[data.targets==9] \n","data.targets = tt\n","data.data = dd\n","data_loader = torch.utils.data.DataLoader(data, batch_size=100, shuffle=True, drop_last =True)\n","# Num batches\n","\n","z = next(iter(data_loader)) \n","\n","\n","for i in range(10):\n","\n","  pattern =  io.imread(os.path.join(save_path, str(i) + '-.png'))\n","  pattern = pattern[35:35+217,113:330,0]\n","  pattern = transform.resize(pattern, (28, 28))\n","  # pattern = torch.from_numpy(pattern)\n","\n","  pattern = torch.from_numpy(pattern)\n","  pattern = pattern.type(torch.FloatTensor)\n","\n","  plt.figure()\n","  plt.subplot(151)\n","  plt.imshow(pattern)\n","\n","  z_old = (z[0] + 1)/2\n","  \n","  plt.subplot(152)\n","  plt.imshow(torch.squeeze(z_old[0,...],0))\n","\n","  y_pred = model(z_old.cuda())\n","  print(y_pred.data.max(1)[1])\n","  \n","  \n","  plt.subplot(153)\n","  freq = y_pred.data.max(1)[1].cpu()\n","  aa = numpy.histogram(freq.numpy()) \n","  y_pos = np.arange(len(aa[0]))\n","  plt.bar(y_pos, aa[0]/aa[0].max())\n","  plt.xticks(y_pos, objects)\n","\n","  \n","  z_new = .3*z_old + pattern\n","\n","  plt.subplot(154)\n","  plt.imshow(torch.squeeze(z_new[0,...],0))\n","\n","  y_pred = model(z_new.cuda())\n","#   print(y_pred.data.max(1)[1])\n","  \n","  plt.subplot(155)\n","  freq = y_pred.data.max(1)[1].cpu()\n","  aa = numpy.histogram(freq.numpy()) \n","  y_pos = np.arange(len(aa[0]))\n","  plt.bar(y_pos, aa[0]/aa[0].max())\n","  plt.xticks(y_pos, objects)\n","  print(y_pred.data.max(1)[1])\n","#   plt.bar()\n","#   plt.hist(y_pred.data.max(1)[1].cpu())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 7, 0, 0, 0, 0, 0, 0,\n","        0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 9, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0,\n","        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0, 0, 0], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 5, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1,\n","        2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1,\n","        1, 1, 1, 1, 1, 2, 2, 2, 7, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1,\n","        1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1,\n","        1, 1, 1, 2], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","        9, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 9, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","        3, 3, 3, 3], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 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9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([5, 5, 9, 7, 5, 5, 5, 7, 5, 9, 7, 7, 5, 9, 5, 7, 7, 7, 7, 9, 5, 9, 7, 9,\n","        9, 7, 5, 5, 5, 9, 5, 5, 7, 5, 9, 5, 5, 5, 5, 5, 9, 7, 7, 5, 7, 5, 5, 5,\n","        9, 5, 5, 5, 5, 7, 7, 5, 7, 9, 7, 7, 5, 9, 9, 7, 5, 9, 7, 9, 9, 7, 7, 5,\n","        7, 9, 7, 5, 5, 5, 5, 9, 7, 5, 5, 7, 5, 5, 5, 5, 5, 5, 2, 7, 5, 5, 5, 5,\n","        9, 5, 5, 7], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([0, 6, 0, 0, 6, 6, 6, 4, 6, 6, 6, 6, 0, 0, 9, 0, 6, 6, 6, 9, 6, 6, 6, 6,\n","        9, 0, 6, 3, 6, 0, 6, 6, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 0, 6, 6, 6, 6,\n","        6, 6, 6, 6, 6, 0, 6, 6, 0, 6, 0, 6, 6, 6, 9, 6, 0, 6, 6, 0, 6, 6, 6, 6,\n","        6, 6, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n","        0, 6, 6, 0], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9], device='cuda:0')\n","tensor([7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n","        7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n","        7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n","        7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n","        7, 7, 7, 7], device='cuda:0')\n","tensor([9, 9, 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device='cuda:0')\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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5mtiPtXAHtmpopzEy+V2Pe3n2beFZfQue5iAJoWdFE8FDpbT8VnIsZndHSU6667jte97nW8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\nVuAnwG8BlwGP1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size 432x288 with 5 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size 432x288 with 5 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DPUgorlsIU5HPdFFetorJnhPGtj3PWO/8dR7/4lZN7J/NEfiCMI9ywftu0558th97K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\nd78HuKfl2G81fH5t2y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8BZGuLulzoc2VWFrqZtRKU+Wfd/Stx9y4zWxuPrwWeXZgmLk28WuHJm29gxWnns/z0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\nzH5iZvfE/f+t7pySmd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bFpcdImljfPF1rnBABsLE/ZLht7wsWRnkonmdBXWTNgjT/Ypq1PF6zOMb22z/J4tde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\n","text/plain":["<Figure size 432x288 with 5 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"sAP_1AyrIJQp","colab_type":"code","outputId":"e0fdbdc1-af5a-49c1-9a31-9eccad9781d4","executionInfo":{"status":"ok","timestamp":1564951868791,"user_tz":240,"elapsed":197,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["  z.min()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(0.)"]},"metadata":{"tags":[]},"execution_count":232}]},{"cell_type":"code","metadata":{"id":"REXMt5SvgdbK","colab_type":"code","outputId":"cf279773-9d32-4b57-dc0a-3c8f0d207efc","executionInfo":{"status":"ok","timestamp":1564869780908,"user_tz":240,"elapsed":313,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["for i in range(10):\n","    print(torch.sum(pred==i))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor(236, device='cuda:0')\n","tensor(3, device='cuda:0')\n","tensor(1874, device='cuda:0')\n","tensor(160, device='cuda:0')\n","tensor(2689, device='cuda:0')\n","tensor(22, device='cuda:0')\n","tensor(44, device='cuda:0')\n","tensor(81, device='cuda:0')\n","tensor(93796, device='cuda:0')\n","tensor(98, device='cuda:0')\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DQa2gDav9kKr","colab_type":"code","outputId":"db3940f6-bccd-4b68-e5d1-990a3e0776e4","executionInfo":{"status":"error","timestamp":1564867442930,"user_tz":240,"elapsed":288,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":300}},"source":["# ls\n","from google.colab import files\n","files.download('./*.png')\n"],"execution_count":0,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-96-9eb9ebc38cb1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./*.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mdownload\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    142\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=undefined-variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m   \u001b[0mstarted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_threading\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEvent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: Cannot find file: ./*.png"]}]},{"cell_type":"code","metadata":{"id":"SBxJ8w3AYAbz","colab_type":"code","colab":{}},"source":["ls drive/My\\ Drive/classification_images\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3GAnOj4gXjd8","colab_type":"code","outputId":"8b431de1-784f-4821-caca-18c31191ec3f","executionInfo":{"status":"ok","timestamp":1564867529154,"user_tz":240,"elapsed":24357,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":[""],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"x1TYqjT17ejE","colab_type":"code","outputId":"ce93940e-d31e-48b7-cdee-26778485fc75","executionInfo":{"status":"ok","timestamp":1564860495533,"user_tz":240,"elapsed":2235,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# z = z[all_idx]\n","pred = torch.cat(all_preds)\n","for i in range(10):\n","    plt.figure()\n","    a = torch.mean(z[pred==i] , dim=0) \n","    a = a.view(-1,28)\n","    b = model(a[None,None,...].cuda())\n","    c = b.data.max(1)[1]\n","    plt.title(f'gt: {str(i)}  -  pred: {str(c.cpu().data[0].numpy())} -  size-gt: {z[pred==i].size(0)}')\n","    plt.imshow(1-a)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAEICAYAAACQ6CLfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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PiKTQ/IZ5C8xPiKTQ/IZ4SNW+/iOQDeBhALgAFsERV7xGROwB8A8Ce8KO3qeor1rLq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\nzU+Ip9D8hHjK/wcNT6gLuPN+1gAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"vs1QSHq79WhR","colab_type":"code","colab":{}},"source":["# for i in range(z.size(0)):\n","#   print(torch.max(data[1,0]))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"5d6-r9IX8wz2","colab_type":"code","outputId":"ce2c98f0-bdba-411e-be73-a539a19e255e","executionInfo":{"status":"ok","timestamp":1565041850498,"user_tz":240,"elapsed":12876,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":901}},"source":["# Repeating the above experiment with VGG and ResNet \n","# VGG16\n","\n","# https://towardsdatascience.com/model-summary-in-pytorch-b5a1e4b64d25\n","\n","\n","import torch\n","from torchvision import models\n","from torchsummary import summary\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","vgg = models.vgg16().to(device)\n","summary(vgg, (3, 224, 224))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1         [-1, 64, 224, 224]           1,792\n","              ReLU-2         [-1, 64, 224, 224]               0\n","            Conv2d-3         [-1, 64, 224, 224]          36,928\n","              ReLU-4         [-1, 64, 224, 224]               0\n","         MaxPool2d-5         [-1, 64, 112, 112]               0\n","            Conv2d-6        [-1, 128, 112, 112]          73,856\n","              ReLU-7        [-1, 128, 112, 112]               0\n","            Conv2d-8        [-1, 128, 112, 112]         147,584\n","              ReLU-9        [-1, 128, 112, 112]               0\n","        MaxPool2d-10          [-1, 128, 56, 56]               0\n","           Conv2d-11          [-1, 256, 56, 56]         295,168\n","             ReLU-12          [-1, 256, 56, 56]               0\n","           Conv2d-13          [-1, 256, 56, 56]         590,080\n","             ReLU-14          [-1, 256, 56, 56]               0\n","           Conv2d-15          [-1, 256, 56, 56]         590,080\n","             ReLU-16          [-1, 256, 56, 56]               0\n","        MaxPool2d-17          [-1, 256, 28, 28]               0\n","           Conv2d-18          [-1, 512, 28, 28]       1,180,160\n","             ReLU-19          [-1, 512, 28, 28]               0\n","           Conv2d-20          [-1, 512, 28, 28]       2,359,808\n","             ReLU-21          [-1, 512, 28, 28]               0\n","           Conv2d-22          [-1, 512, 28, 28]       2,359,808\n","             ReLU-23          [-1, 512, 28, 28]               0\n","        MaxPool2d-24          [-1, 512, 14, 14]               0\n","           Conv2d-25          [-1, 512, 14, 14]       2,359,808\n","             ReLU-26          [-1, 512, 14, 14]               0\n","           Conv2d-27          [-1, 512, 14, 14]       2,359,808\n","             ReLU-28          [-1, 512, 14, 14]               0\n","           Conv2d-29          [-1, 512, 14, 14]       2,359,808\n","             ReLU-30          [-1, 512, 14, 14]               0\n","        MaxPool2d-31            [-1, 512, 7, 7]               0\n","AdaptiveAvgPool2d-32            [-1, 512, 7, 7]               0\n","           Linear-33                 [-1, 4096]     102,764,544\n","             ReLU-34                 [-1, 4096]               0\n","          Dropout-35                 [-1, 4096]               0\n","           Linear-36                 [-1, 4096]      16,781,312\n","             ReLU-37                 [-1, 4096]               0\n","          Dropout-38                 [-1, 4096]               0\n","           Linear-39                 [-1, 1000]       4,097,000\n","================================================================\n","Total params: 138,357,544\n","Trainable params: 138,357,544\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.57\n","Forward/backward pass size (MB): 218.78\n","Params size (MB): 527.79\n","Estimated Total Size (MB): 747.15\n","----------------------------------------------------------------\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Mq1ETOZCwzrU","colab_type":"code","outputId":"3388ce2e-b775-4007-a554-f4b452e524e7","executionInfo":{"status":"error","timestamp":1565041883791,"user_tz":240,"elapsed":396,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":232}},"source":["# ResNet\n","\n","# https://zablo.net/blog/post/using-resnet-for-mnist-in-pytorch-tutorial/\n","\n","\n","def resnet18(pretrained=False, **kwargs):\n","    \"\"\"Constructs a ResNet-18 model.\n","    Args:\n","        pretrained (bool): If True, returns a model pre-trained on ImageNet\n","    \"\"\"\n","    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)\n","    if pretrained:\n","        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))\n","    return model\n","  \n","  \n","\n","def forward(self, x):\n","    x = self.conv1(x)\n","    x = self.bn1(x)\n","    ## ... skipped a few lines ...\n","    return x\n","  \n","self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n","\n","\n","\n","class MnistResNet(ResNet):\n","    def __init__(self):\n","        super(MnistResNet, self).__init__(BasicBlock, [2, 2, 2, 2], num_classes=10)\n","        self.conv1 = torch.nn.Conv2d(1, 64, \n","            kernel_size=(7, 7), \n","            stride=(2, 2), \n","            padding=(3, 3), bias=False)\n","        \n","    def forward(self, x):\n","        return torch.softmax(\n","            super(MnistResNet, self).forward(x), dim=-1)\n","\n","      \n","      \n","from torchvision.models.resnet import ResNet, BasicBlock\n","from torchvision.datasets import MNIST\n","from tqdm.autonotebook import tqdm\n","from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n","import inspect\n","import time\n","from torch import nn, optim\n","import torch\n","from torchvision.transforms import Compose, ToTensor, Normalize, Resize\n","from torch.utils.data import DataLoader\n","\n","\n","\n"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-2-2e5f8fba3444>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'nn' is not defined"]}]},{"cell_type":"code","metadata":{"id":"fvwgq7ECw_i5","colab_type":"code","outputId":"372ae30b-5b05-483c-9013-d51f93ff7cad","executionInfo":{"status":"error","timestamp":1565041968832,"user_tz":240,"elapsed":300,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":130}},"source":["# model:\n","model = YourModelHere()\n","\n","# params you need to specify:\n","epochs = 5\n","train_loader, val_loader = # put your data loader here\n","loss_function = nn.CrossEntropyLoss() # your loss function, cross entropy works well for multi-class problems\n","\n","# optimizer, I've used Adadelta, as it wokrs well without any magic numbers\n","optimizer = optim.Adadelta(model.parameters())\n","\n","start_ts = time.time()\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","\n","losses = []\n","batches = len(train_loader)\n","val_batches = len(val_loader)\n","\n","# loop for every epoch (training + evaluation)\n","for epoch in range(epochs):\n","    total_loss = 0\n","\n","    # progress bar (works in Jupyter notebook too!)\n","    progress = tqdm(enumerate(train_loader), desc=\"Loss: \", total=batches)\n","\n","    # ----------------- TRAINING  -------------------- \n","    # set model to training\n","    model.train()\n","    \n","    for i, data in progress:\n","        X, y = data[0].to(device), data[1].to(device)\n","        \n","        # training step for single batch\n","        model.zero_grad()\n","        outputs = model(X)\n","        loss = loss_function(outputs, y)\n","        loss.backward()\n","        optimizer.step()\n","\n","        # getting training quality data\n","        current_loss = loss.item()\n","        total_loss += current_loss\n","\n","        # updating progress bar\n","        progress.set_description(\"Loss: {:.4f}\".format(total_loss/(i+1)))\n","        \n","    # releasing unceseccary memory in GPU\n","    if torch.cuda.is_available():\n","        torch.cuda.empty_cache()\n","    \n","    # ----------------- VALIDATION  ----------------- \n","    val_losses = 0\n","    precision, recall, f1, accuracy = [], [], [], []\n","    \n","    # set model to evaluating (testing)\n","    model.eval()\n","    with torch.no_grad():\n","        for i, data in enumerate(val_loader):\n","            X, y = data[0].to(device), data[1].to(device)\n","\n","            outputs = model(X) # this get's the prediction from the network\n","\n","            val_losses += loss_function(outputs, y)\n","\n","            predicted_classes = torch.max(outputs, 1)[1] # get class from network's prediction\n","            \n","            # calculate P/R/F1/A metrics for batch\n","            for acc, metric in zip((precision, recall, f1, accuracy), \n","                                   (precision_score, recall_score, f1_score, accuracy_score)):\n","                acc.append(\n","                    calculate_metric(metric, y.cpu(), predicted_classes.cpu())\n","                )\n","          \n","    print(f\"Epoch {epoch+1}/{epochs}, training loss: {total_loss/batches}, validation loss: {val_losses/val_batches}\")\n","    print_scores(precision, recall, f1, accuracy, val_batches)\n","    losses.append(total_loss/batches) # for plotting learning curve\n","print(f\"Training time: {time.time()-start_ts}s\")"],"execution_count":0,"outputs":[{"output_type":"error","ename":"SyntaxError","evalue":"ignored","traceback":["\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-1feda64921d6>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    train_loader, val_loader = # put your data loader here\u001b[0m\n\u001b[0m                                                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"]}]},{"cell_type":"code","metadata":{"id":"z_SdFi7IxUVV","colab_type":"code","colab":{}},"source":["def calculate_metric(metric_fn, true_y, pred_y):\n","    # multi class problems need to have averaging method\n","    if \"average\" in inspect.getfullargspec(metric_fn).args:\n","        return metric_fn(true_y, pred_y, average=\"macro\")\n","    else:\n","        return metric_fn(true_y, pred_y)\n","    \n","def print_scores(p, r, f1, a, batch_size):\n","    # just an utility printing function\n","    for name, scores in zip((\"precision\", \"recall\", \"F1\", \"accuracy\"), (p, r, f1, a)):\n","        print(f\"\\t{name.rjust(14, ' ')}: {sum(scores)/batch_size:.4f}\")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Zw9bbgKaxVrc","colab_type":"code","colab":{}},"source":["def get_data_loaders(train_batch_size, val_batch_size):\n","    mnist = MNIST(download=False, train=True, root=\".\").train_data.float()\n","    \n","    data_transform = Compose([ Resize((224, 224)),ToTensor(), Normalize((mnist.mean()/255,), (mnist.std()/255,))])\n","\n","    train_loader = DataLoader(MNIST(download=True, root=\".\", transform=data_transform, train=True),\n","                              batch_size=train_batch_size, shuffle=True)\n","\n","    val_loader = DataLoader(MNIST(download=False, root=\".\", transform=data_transform, train=False),\n","                            batch_size=val_batch_size, shuffle=False)\n","    return train_loader, val_loader"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"W4gOElrmBLwW","colab_type":"code","outputId":"a42c1c64-1fa1-4db3-e50a-c80bb9c19f2c","executionInfo":{"status":"ok","timestamp":1570746332024,"user_tz":240,"elapsed":529,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":268}},"source":["plt.style.use('ggplot')\n","# create a color palette\n","palette = plt.get_cmap('Set1')\n","plt.style.use('seaborn-darkgrid')\n","\n","\n","# noise  \n","a = np.array([0.1       , 0.11273001, 0.12738   , 0.14923   , 0.17384002,\n","       0.20746   , 0.27083001, 0.42860001, 0.66631001, 0.74269998,\n","       0.89999998])\n","\n","\n","# a_err = np.array([2.38418579e-08, 1.29856066e-04, 5.15420195e-04, 4.79894201e-04,\n","#        8.44168944e-04, 9.09090615e-04, 3.60781164e-04, 6.25288753e-04,\n","#        6.66184683e-04, 7.34829166e-04, 0.00000000e+00])\n","\n","\n","\n","# a = 1-a\n","\n","# signal\n","b = [0.1,\n","0.10007,\n","0.1002,\n","0.10037999,\n","0.10102,\n","0.10197,\n","0.104949996,\n","0.12264,\n","0.21420999,\n","0.58678997,\n",".9\n","]\n","\n","\n","\n","\n","# b = b[::-1]\n","\n","plt.figure()\n","plt.figure(figsize=(8,3.5))\n","N = 11\n","men_means = a\n","women_means = np.asarray(b)\n","\n","ind = np.arange(N) \n","width = 0.35       \n","plt.bar(ind, men_means, width, label='(1-γ) noise + γ bias')#, yerr=a_err)\n","plt.bar(ind + width, women_means, width,\n","    label='(1-γ) signal + γ bias')\n","\n","plt.ylabel('accuracy')\n","# plt.title('(1-γ) A + γ Bias')\n","\n","plt.xticks(ind + width / 2, )\n","plt.xticks(ind + width / 2, ['{:02.1f}'.format(i) for i in np.linspace(0,1,N)] )\n","plt.legend(loc='best', fontsize=13)\n","plt.xlabel('γ')\n","plt.show()\n","\n","\n","\n"],"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAe4AAADqCAYAAABk8ab0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nL/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A9PR3r16/HvXv3MHPmzGrnnz59utawu7t7uWmaNGnCa7iJiIiqodNZ5TNmzEBZWRne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\nWNxEREQi8v+DunEa7rMR7QAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 576x252 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"hubylbyZCWqD","colab_type":"code","outputId":"7f98869e-b6f4-4c67-a13e-d361640ba43e","executionInfo":{"status":"ok","timestamp":1570746317535,"user_tz":240,"elapsed":640,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":268}},"source":["plt.style.use('ggplot')\n","# create a color palette\n","palette = plt.get_cmap('Set1')\n","plt.style.use('seaborn-darkgrid')\n","\n","\n","\n","# γ\n","# len(a)\n","\n","\n","\n","# with mean images\n","\n","\n","a = np.array([0.09999999, 0.12702   , 0.17915002, 0.31265   , 0.61754   ,\n","       0.84162009, 0.98592997, 0.99996996, 1.        , 1.        ,\n","       1.        ])\n","\n","\n","# a_err = np.array([0.00000000e+00, 7.80569100e-04, 9.68813166e-04, 2.13597141e-03,\n","#        1.70586286e-03, 2.99506070e-04, 6.11228336e-05, 0.00000000e+00,\n","#        0.00000000e+00, 0.00000000e+00, 0.00000000e+00])\n","\n","\n","# a = 1-a\n","\n","\n","b = [0.1,\n","0.10064,\n","0.102560006,\n","0.109469995,\n","0.149,\n","0.27971,\n","0.53397,\n","0.83037996,\n","0.99373007,\n","1,\n","1\n","]\n","\n","\n","# b = b[::-1]\n","\n","\n","plt.figure()\n","plt.figure(figsize=(8,3.5))\n","N = 11\n","men_means = a\n","women_means = np.asarray(b)\n","\n","ind = np.arange(N) \n","width = 0.35       \n","plt.bar(ind, men_means, width, label='(1-γ) noise + γ mean') #, yerr = a_err)\n","plt.bar(ind + width, women_means, width,\n","    label='(1-γ) signal + γ mean')\n","\n","plt.ylabel('accuracy')\n","# plt.title('(1-γ) A + γ Bias')\n","\n","plt.xticks(ind + width / 2, )\n","plt.xticks(ind + width / 2, ['{:02.1f}'.format(i) for i in np.linspace(0,1,N)] )\n","plt.legend(loc='best', fontsize=13)\n","plt.xlabel('γ')\n","plt.show()\n","# plt.\n","\n"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAe4AAADqCAYAAABk8ab0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxU1ERCQi/x/GuuOKxAiwzwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 576x252 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"vM-cGLOWBihx","colab_type":"code","outputId":"0f45adf1-e30c-4488-b622-9fc384848030","executionInfo":{"status":"ok","timestamp":1566188068253,"user_tz":240,"elapsed":470,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["np.linspace(.1,1,10)\n","str(np.linspace(.1,1,10) )\n","\n","indx = ['{:02.1f}'.format(i) for i in np.linspace(.1,1,10)]\n","indx"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1.0']"]},"metadata":{"tags":[]},"execution_count":186}]},{"cell_type":"code","metadata":{"id":"jFqdqnInEytx","colab_type":"code","outputId":"b546fcfb-35b7-4f6e-aaec-e2d349522516","executionInfo":{"status":"ok","timestamp":1566187987669,"user_tz":240,"elapsed":531,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# {':2f'}.format(3.21)\n","'{:06.2f}'.format(3.141592653589793)\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'003.14'"]},"metadata":{"tags":[]},"execution_count":178}]},{"cell_type":"code","metadata":{"id":"bYuknU15DKbt","colab_type":"code","outputId":"4ed50794-2522-4cff-a0c1-8c31c7574e5d","executionInfo":{"status":"ok","timestamp":1566187706980,"user_tz":240,"elapsed":835,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":281}},"source":["%matplotlib inline\n","N = 5\n","men_means = (20, 35, 30, 35, 27)\n","women_means = (25, 32, 34, 20, 25)\n","\n","ind = np.arange(N) \n","width = 0.35       \n","plt.bar(ind, men_means, width, label='Men')\n","plt.bar(ind + width, women_means, width,\n","    label='Women')\n","\n","plt.ylabel('Scores')\n","plt.title('Scores by group and gender')\n","\n","plt.xticks(ind + width / 2, ('G1', 'G2', 'G3', 'G4', 'G5'))\n","plt.legend(loc='best')\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEICAYAAABS0fM3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"6lvYlmtIDtnl","colab_type":"code","colab":{}},"source":["\n","\n","\n","# noise  \n","a = np.array([0.1       , 0.11273001, 0.12738   , 0.14923   , 0.17384002,\n","       0.20746   , 0.27083001, 0.42860001, 0.66631001, 0.74269998,\n","       0.89999998])\n","\n","\n","# a_err = np.array([2.38418579e-08, 1.29856066e-04, 5.15420195e-04, 4.79894201e-04,\n","#        8.44168944e-04, 9.09090615e-04, 3.60781164e-04, 6.25288753e-04,\n","#        6.66184683e-04, 7.34829166e-04, 0.00000000e+00])\n","\n","\n","\n","# a = 1-a\n","\n","# signal\n","b = [0.1,\n","0.10007,\n","0.1002,\n","0.10037999,\n","0.10102,\n","0.10197,\n","0.104949996,\n","0.12264,\n","0.21420999,\n","0.58678997,\n",".9\n","]\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ivWA4wTKf7uZ","colab_type":"code","outputId":"c8a97c5f-d351-4d7f-d2a5-8ae37516cf3b","executionInfo":{"status":"error","timestamp":1568996930556,"user_tz":240,"elapsed":799,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":181}},"source":["for i in a:\n","  print('{0.03f}'.format(str(i)))"],"execution_count":0,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-12-8bba9ba876b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'{0.03f}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute '03f'"]}]},{"cell_type":"code","metadata":{"id":"l7qLkG9mf_k-","colab_type":"code","outputId":"3948e4dc-6cd9-40a6-e701-b4e830ab6f4c","executionInfo":{"status":"error","timestamp":1568997087184,"user_tz":240,"elapsed":788,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":181}},"source":["rr = numpy.round(b,3)\n","'&'.join(list(rr))"],"execution_count":0,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-20-8a8c7e977d4d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mrr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;34m'&'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mTypeError\u001b[0m: sequence item 0: expected str instance, numpy.float64 found"]}]},{"cell_type":"code","metadata":{"id":"QA4HW6AagbOh","colab_type":"code","outputId":"9190102e-c35f-4fe3-eec0-8f036c88611a","executionInfo":{"status":"ok","timestamp":1568997270622,"user_tz":240,"elapsed":592,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["rr = numpy.round(b,3)\n","' & '.join([str(k) for k in list(rr)])"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'0.1 & 0.101 & 0.103 & 0.109 & 0.149 & 0.28 & 0.534 & 0.83 & 0.994 & 1.0 & 1.0'"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"code","metadata":{"id":"s5M_cNdJg7Bb","colab_type":"code","outputId":"b603b457-c291-482a-9a64-fb7df605d7d3","executionInfo":{"status":"error","timestamp":1568997112621,"user_tz":240,"elapsed":871,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":164}},"source":["''.join(list(rr))"],"execution_count":0,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-22-11f9d9f5774b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mTypeError\u001b[0m: sequence item 0: expected str instance, numpy.float64 found"]}]},{"cell_type":"code","metadata":{"id":"5GRV5FkIg_co","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}